Kai-Wei Chang


2020

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“The Boating Store Had Its Best Sail Ever”: Pronunciation-attentive Contextualized Pun Recognition
Yichao Zhou | Jyun-Yu Jiang | Jieyu Zhao | Kai-Wei Chang | Wei Wang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Humor plays an important role in human languages and it is essential to model humor when building intelligence systems. Among different forms of humor, puns perform wordplay for humorous effects by employing words with double entendre and high phonetic similarity. However, identifying and modeling puns are challenging as puns usually involved implicit semantic or phonological tricks. In this paper, we propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor, detect if a sentence contains puns and locate them in the sentence. PCPR derives contextualized representation for each word in a sentence by capturing the association between the surrounding context and its corresponding phonetic symbols. Extensive experiments are conducted on two benchmark datasets. Results demonstrate that the proposed approach significantly outperforms the state-of-the-art methods in pun detection and location tasks. In-depth analyses verify the effectiveness and robustness of PCPR.

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Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer
Jieyu Zhao | Subhabrata Mukherjee | Saghar Hosseini | Kai-Wei Chang | Ahmed Hassan Awadallah
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Multilingual representations embed words from many languages into a single semantic space such that words with similar meanings are close to each other regardless of the language. These embeddings have been widely used in various settings, such as cross-lingual transfer, where a natural language processing (NLP) model trained on one language is deployed to another language. While the cross-lingual transfer techniques are powerful, they carry gender bias from the source to target languages. In this paper, we study gender bias in multilingual embeddings and how it affects transfer learning for NLP applications. We create a multilingual dataset for bias analysis and propose several ways for quantifying bias in multilingual representations from both the intrinsic and extrinsic perspectives. Experimental results show that the magnitude of bias in the multilingual representations changes differently when we align the embeddings to different target spaces and that the alignment direction can also have an influence on the bias in transfer learning. We further provide recommendations for using the multilingual word representations for downstream tasks.

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Mitigating Gender Bias Amplification in Distribution by Posterior Regularization
Shengyu Jia | Tao Meng | Jieyu Zhao | Kai-Wei Chang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Advanced machine learning techniques have boosted the performance of natural language processing. Nevertheless, recent studies, e.g., (CITATION) show that these techniques inadvertently capture the societal bias hidden in the corpus and further amplify it. However, their analysis is conducted only on models’ top predictions. In this paper, we investigate the gender bias amplification issue from the distribution perspective and demonstrate that the bias is amplified in the view of predicted probability distribution over labels. We further propose a bias mitigation approach based on posterior regularization. With little performance loss, our method can almost remove the bias amplification in the distribution. Our study sheds the light on understanding the bias amplification.

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Towards Understanding Gender Bias in Relation Extraction
Andrew Gaut | Tony Sun | Shirlyn Tang | Yuxin Huang | Jing Qian | Mai ElSherief | Jieyu Zhao | Diba Mirza | Elizabeth Belding | Kai-Wei Chang | William Yang Wang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent developments in Neural Relation Extraction (NRE) have made significant strides towards Automated Knowledge Base Construction. While much attention has been dedicated towards improvements in accuracy, there have been no attempts in the literature to evaluate social biases exhibited in NRE systems. In this paper, we create WikiGenderBias, a distantly supervised dataset composed of over 45,000 sentences including a 10% human annotated test set for the purpose of analyzing gender bias in relation extraction systems. We find that when extracting spouse-of and hypernym (i.e., occupation) relations, an NRE system performs differently when the gender of the target entity is different. However, such disparity does not appear when extracting relations such as birthDate or birthPlace. We also analyze how existing bias mitigation techniques, such as name anonymization, word embedding debiasing, and data augmentation affect the NRE system in terms of maintaining the test performance and reducing biases. Unfortunately, due to NRE models rely heavily on surface level cues, we find that existing bias mitigation approaches have a negative effect on NRE. Our analysis lays groundwork for future quantifying and mitigating bias in NRE.

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On the Robustness of Language Encoders against Grammatical Errors
Fan Yin | Quanyu Long | Tao Meng | Kai-Wei Chang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We conduct a thorough study to diagnose the behaviors of pre-trained language encoders (ELMo, BERT, and RoBERTa) when confronted with natural grammatical errors. Specifically, we collect real grammatical errors from non-native speakers and conduct adversarial attacks to simulate these errors on clean text data. We use this approach to facilitate debugging models on downstream applications. Results confirm that the performance of all tested models is affected but the degree of impact varies. To interpret model behaviors, we further design a linguistic acceptability task to reveal their abilities in identifying ungrammatical sentences and the position of errors. We find that fixed contextual encoders with a simple classifier trained on the prediction of sentence correctness are able to locate error positions. We also design a cloze test for BERT and discover that BERT captures the interaction between errors and specific tokens in context. Our results shed light on understanding the robustness and behaviors of language encoders against grammatical errors.

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SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics
Da Yin | Tao Meng | Kai-Wei Chang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We propose SentiBERT, a variant of BERT that effectively captures compositional sentiment semantics. The model incorporates contextualized representation with binary constituency parse tree to capture semantic composition. Comprehensive experiments demonstrate that SentiBERT achieves competitive performance on phrase-level sentiment classification. We further demonstrate that the sentiment composition learned from the phrase-level annotations on SST can be transferred to other sentiment analysis tasks as well as related tasks, such as emotion classification tasks. Moreover, we conduct ablation studies and design visualization methods to understand SentiBERT. We show that SentiBERT is better than baseline approaches in capturing negation and the contrastive relation and model the compositional sentiment semantics.

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A Transformer-based Approach for Source Code Summarization
Wasi Ahmad | Saikat Chakraborty | Baishakhi Ray | Kai-Wei Chang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Generating a readable summary that describes the functionality of a program is known as source code summarization. In this task, learning code representation by modeling the pairwise relationship between code tokens to capture their long-range dependencies is crucial. To learn code representation for summarization, we explore the Transformer model that uses a self-attention mechanism and has shown to be effective in capturing long-range dependencies. In this work, we show that despite the approach is simple, it outperforms the state-of-the-art techniques by a significant margin. We perform extensive analysis and ablation studies that reveal several important findings, e.g., the absolute encoding of source code tokens’ position hinders, while relative encoding significantly improves the summarization performance. We have made our code publicly available to facilitate future research.

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What Does BERT with Vision Look At?
Liunian Harold Li | Mark Yatskar | Da Yin | Cho-Jui Hsieh | Kai-Wei Chang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Pre-trained visually grounded language models such as ViLBERT, LXMERT, and UNITER have achieved significant performance improvement on vision-and-language tasks but what they learn during pre-training remains unclear. In this work, we demonstrate that certain attention heads of a visually grounded language model actively ground elements of language to image regions. Specifically, some heads can map entities to image regions, performing the task known as entity grounding. Some heads can even detect the syntactic relations between non-entity words and image regions, tracking, for example, associations between verbs and regions corresponding to their arguments. We denote this ability as syntactic grounding. We verify grounding both quantitatively and qualitatively, using Flickr30K Entities as a testbed.

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PolicyQA: A Reading Comprehension Dataset for Privacy Policies
Wasi Ahmad | Jianfeng Chi | Yuan Tian | Kai-Wei Chang
Findings of the Association for Computational Linguistics: EMNLP 2020

Privacy policy documents are long and verbose. A question answering (QA) system can assist users in finding the information that is relevant and important to them. Prior studies in this domain frame the QA task as retrieving the most relevant text segment or a list of sentences from the policy document given a question. On the contrary, we argue that providing users with a short text span from policy documents reduces the burden of searching the target information from a lengthy text segment. In this paper, we present PolicyQA, a dataset that contains 25,017 reading comprehension style examples curated from an existing corpus of 115 website privacy policies. PolicyQA provides 714 human-annotated questions written for a wide range of privacy practices. We evaluate two existing neural QA models and perform rigorous analysis to reveal the advantages and challenges offered by PolicyQA.

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Cross-Lingual Dependency Parsing by POS-Guided Word Reordering
Lu Liu | Yi Zhou | Jianhan Xu | Xiaoqing Zheng | Kai-Wei Chang | Xuanjing Huang
Findings of the Association for Computational Linguistics: EMNLP 2020

We propose a novel approach to cross-lingual dependency parsing based on word reordering. The words in each sentence of a source language corpus are rearranged to meet the word order in a target language under the guidance of a part-of-speech based language model (LM). To obtain the highest reordering score under the LM, a population-based optimization algorithm and its genetic operators are designed to deal with the combinatorial nature of such word reordering. A parser trained on the reordered corpus then can be used to parse sentences in the target language. We demonstrate through extensive experimentation that our approach achieves better or comparable results across 25 target languages (1.73% increase in average), and outperforms a baseline by a significant margin on the languages that are greatly different from the source one. For example, when transferring the English parser to Hindi and Latin, our approach outperforms the baseline by 15.3% and 6.7% respectively.

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Towards Controllable Biases in Language Generation
Emily Sheng | Kai-Wei Chang | Prem Natarajan | Nanyun Peng
Findings of the Association for Computational Linguistics: EMNLP 2020

We present a general approach towards controllable societal biases in natural language generation (NLG). Building upon the idea of adversarial triggers, we develop a method to induce societal biases in generated text when input prompts contain mentions of specific demographic groups. We then analyze two scenarios: 1) inducing negative biases for one demographic and positive biases for another demographic, and 2) equalizing biases between demographics. The former scenario enables us to detect the types of biases present in the model. Specifically, we show the effectiveness of our approach at facilitating bias analysis by finding topics that correspond to demographic inequalities in generated text and comparing the relative effectiveness of inducing biases for different demographics. The second scenario is useful for mitigating biases in downstream applications such as dialogue generation. In our experiments, the mitigation technique proves to be effective at equalizing the amount of biases across demographics while simultaneously generating less negatively biased text overall.

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LOGAN: Local Group Bias Detection by Clustering
Jieyu Zhao | Kai-Wei Chang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Machine learning techniques have been widely used in natural language processing (NLP). However, as revealed by many recent studies, machine learning models often inherit and amplify the societal biases in data. Various metrics have been proposed to quantify biases in model predictions. In particular, several of them evaluate disparity in model performance between protected groups and advantaged groups in the test corpus. However, we argue that evaluating bias at the corpus level is not enough for understanding how biases are embedded in a model. In fact, a model with similar aggregated performance between different groups on the entire data may behave differently on instances in a local region. To analyze and detect such local bias, we propose LOGAN, a new bias detection technique based on clustering. Experiments on toxicity classification and object classification tasks show that LOGAN identifies bias in a local region and allows us to better analyze the biases in model predictions.

2019

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Efficient Contextual Representation Learning With Continuous Outputs
Liunian Harold Li | Patrick H. Chen | Cho-Jui Hsieh | Kai-Wei Chang
Transactions of the Association for Computational Linguistics, Volume 7

Contextual representation models have achieved great success in improving various downstream natural language processing tasks. However, these language-model-based encoders are difficult to train due to their large parameter size and high computational complexity. By carefully examining the training procedure, we observe that the softmax layer, which predicts a distribution of the target word, often induces significant overhead, especially when the vocabulary size is large. Therefore, we revisit the design of the output layer and consider directly predicting the pre-trained embedding of the target word for a given context. When applied to ELMo, the proposed approach achieves a 4-fold speedup and eliminates 80% trainable parameters while achieving competitive performance on downstream tasks. Further analysis shows that the approach maintains the speed advantage under various settings, even when the sentence encoder is scaled up.

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Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing
Tao Meng | Nanyun Peng | Kai-Wei Chang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Prior work on cross-lingual dependency parsing often focuses on capturing the commonalities between source and target languages and overlook the potential to leverage the linguistic properties of the target languages to facilitate the transfer. In this paper, we show that weak supervisions of linguistic knowledge for the target languages can improve a cross-lingual graph-based dependency parser substantially. Specifically, we explore several types of corpus linguistic statistics and compile them into corpus-statistics constraints to facilitate the inference procedure. We propose new algorithms that adapt two techniques, Lagrangian relaxation and posterior regularization, to conduct inference with corpus-statistics constraints. Experiments show that the Lagrangian relaxation and posterior regularization techniques improve the performances on 15 and 17 out of 19 target languages, respectively. The improvements are especially large for the target languages that have different word order features from the source language.

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Robust Text Classifier on Test-Time Budgets
Md Rizwan Parvez | Tolga Bolukbasi | Kai-Wei Chang | Venkatesh Saligrama
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We design a generic framework for learning a robust text classification model that achieves high accuracy under different selection budgets (a.k.a selection rates) at test-time. We take a different approach from existing methods and learn to dynamically filter a large fraction of unimportant words by a low-complexity selector such that any high-complexity state-of-art classifier only needs to process a small fraction of text, relevant for the target task. To this end, we propose a data aggregation method to train the classifier, allowing it to achieve competitive performance on fractured sentences. On four benchmark text classification tasks, we demonstrate that the framework gains consistent speedup with little degradation in accuracy on various selection budgets.

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Retrofitting Contextualized Word Embeddings with Paraphrases
Weijia Shi | Muhao Chen | Pei Zhou | Kai-Wei Chang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Contextualized word embeddings, such as ELMo, provide meaningful representations for words and their contexts. They have been shown to have a great impact on downstream applications. However, we observe that the contextualized embeddings of a word might change drastically when its contexts are paraphrased. As these embeddings are over-sensitive to the context, the downstream model may make different predictions when the input sentence is paraphrased. To address this issue, we propose a post-processing approach to retrofit the embedding with paraphrases. Our method learns an orthogonal transformation on the input space of the contextualized word embedding model, which seeks to minimize the variance of word representations on paraphrased contexts. Experiments show that the proposed method significantly improves ELMo on various sentence classification and inference tasks.

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The Woman Worked as a Babysitter: On Biases in Language Generation
Emily Sheng | Kai-Wei Chang | Premkumar Natarajan | Nanyun Peng
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups. In this work, we introduce the notion of the regard towards a demographic, use the varying levels of regard towards different demographics as a defining metric for bias in NLG, and analyze the extent to which sentiment scores are a relevant proxy metric for regard. To this end, we collect strategically-generated text from language models and manually annotate the text with both sentiment and regard scores. Additionally, we build an automatic regard classifier through transfer learning, so that we can analyze biases in unseen text. Together, these methods reveal the extent of the biased nature of language model generations. Our analysis provides a study of biases in NLG, bias metrics and correlated human judgments, and empirical evidence on the usefulness of our annotated dataset.

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Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification
Yichao Zhou | Jyun-Yu Jiang | Kai-Wei Chang | Wei Wang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Adversarial attacks against machine learning models have threatened various real-world applications such as spam filtering and sentiment analysis. In this paper, we propose a novel framework, learning to discriminate perturbations (DISP), to identify and adjust malicious perturbations, thereby blocking adversarial attacks for text classification models. To identify adversarial attacks, a perturbation discriminator validates how likely a token in the text is perturbed and provides a set of potential perturbations. For each potential perturbation, an embedding estimator learns to restore the embedding of the original word based on the context and a replacement token is chosen based on approximate kNN search. DISP can block adversarial attacks for any NLP model without modifying the model structure or training procedure. Extensive experiments on two benchmark datasets demonstrate that DISP significantly outperforms baseline methods in blocking adversarial attacks for text classification. In addition, in-depth analysis shows the robustness of DISP across different situations.

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Examining Gender Bias in Languages with Grammatical Gender
Pei Zhou | Weijia Shi | Jieyu Zhao | Kuan-Hao Huang | Muhao Chen | Ryan Cotterell | Kai-Wei Chang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Recent studies have shown that word embeddings exhibit gender bias inherited from the training corpora. However, most studies to date have focused on quantifying and mitigating such bias only in English. These analyses cannot be directly extended to languages that exhibit morphological agreement on gender, such as Spanish and French. In this paper, we propose new metrics for evaluating gender bias in word embeddings of these languages and further demonstrate evidence of gender bias in bilingual embeddings which align these languages with English. Finally, we extend an existing approach to mitigate gender bias in word embedding of these languages under both monolingual and bilingual settings. Experiments on modified Word Embedding Association Test, word similarity, word translation, and word pair translation tasks show that the proposed approaches can effectively reduce the gender bias while preserving the utility of the original embeddings.

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Bias and Fairness in Natural Language Processing
Kai-Wei Chang | Vinod Prabhakaran | Vicente Ordonez
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts

Recent advances in data-driven machine learning techniques (e.g., deep neural networks) have revolutionized many natural language processing applications. These approaches automatically learn how to make decisions based on the statistics and diagnostic information from large amounts of training data. Despite the remarkable accuracy of machine learning in various applications, learning algorithms run the risk of relying on societal biases encoded in the training data to make predictions. This often occurs even when gender and ethnicity information is not explicitly provided to the system because learning algorithms are able to discover implicit associations between individuals and their demographic information based on other variables such as names, titles, home addresses, etc. Therefore, machine learning algorithms risk potentially encouraging unfair and discriminatory decision making and raise serious privacy concerns. Without properly quantifying and reducing the reliance on such correlations, broad adoption of these models might have the undesirable effect of magnifying harmful stereotypes or implicit biases that rely on sensitive demographic attributes.In this tutorial, we will review the history of bias and fairness studies in machine learning and language processing and present recent community effort in quantifying and mitigating bias in natural language processing models for a wide spectrum of tasks, including word embeddings, co-reference resolution, machine translation, and vision-and-language tasks. In particular, we will focus on the following topics:+ Definitions of fairness and bias.+ Data, algorithms, and models that propagate and even amplify social bias to NLP applications and metrics to quantify these biases.+ Algorithmic solutions; learning objective; design principles to prevent social bias in NLP systems and their potential drawbacks.The tutorial will bring researchers and practitioners to be aware of this issue, and encourage the research community to propose innovative solutions to promote fairness in NLP.

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Visualizing Trends of Key Roles in News Articles
Chen Xia | Haoxiang Zhang | Jacob Moghtader | Allen Wu | Kai-Wei Chang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

There are tons of news generated every day reflecting the change of key roles such as people, organizations and political parties. Analyzing the trend of these key roles can help understand the information flow in a more effective way. In this paper, we present a demonstration system that visualizes the news trend of key roles based on natural language processing techniques. Specifically, we apply semantic role labelling to understand relationships between key roles in the news. We also train a dynamic word embedding model to align representations of words in different time periods to measure how the similarities between a key role and news topics change over time. Note: The github link to our demo jupyter notebook and screencast video is https://github.com/kasinxc/Visualizing-Trend-of-Key-Roles-in-News-Articles

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Learning to Represent Bilingual Dictionaries
Muhao Chen | Yingtao Tian | Haochen Chen | Kai-Wei Chang | Steven Skiena | Carlo Zaniolo
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Bilingual word embeddings have been widely used to capture the correspondence of lexical semantics in different human languages. However, the cross-lingual correspondence between sentences and words is less studied, despite that this correspondence can significantly benefit many applications such as crosslingual semantic search and textual inference. To bridge this gap, we propose a neural embedding model that leverages bilingual dictionaries. The proposed model is trained to map the lexical definitions to the cross-lingual target words, for which we explore with different sentence encoding techniques. To enhance the learning process on limited resources, our model adopts several critical learning strategies, including multi-task learning on different bridges of languages, and joint learning of the dictionary model with a bilingual word embedding model. We conduct experiments on two new tasks. In the cross-lingual reverse dictionary retrieval task, we demonstrate that our model is capable of comprehending bilingual concepts based on descriptions, and the proposed learning strategies are effective. In the bilingual paraphrase identification task, we show that our model effectively associates sentences in different languages via a shared embedding space, and outperforms existing approaches in identifying bilingual paraphrases.

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Cross-Lingual Dependency Parsing with Unlabeled Auxiliary Languages
Wasi Uddin Ahmad | Zhisong Zhang | Xuezhe Ma | Kai-Wei Chang | Nanyun Peng
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Cross-lingual transfer learning has become an important weapon to battle the unavailability of annotated resources for low-resource languages. One of the fundamental techniques to transfer across languages is learning language-agnostic representations, in the form of word embeddings or contextual encodings. In this work, we propose to leverage unannotated sentences from auxiliary languages to help learning language-agnostic representations. Specifically, we explore adversarial training for learning contextual encoders that produce invariant representations across languages to facilitate cross-lingual transfer. We conduct experiments on cross-lingual dependency parsing where we train a dependency parser on a source language and transfer it to a wide range of target languages. Experiments on 28 target languages demonstrate that adversarial training significantly improves the overall transfer performances under several different settings. We conduct a careful analysis to evaluate the language-agnostic representations resulted from adversarial training.

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Mitigating Gender Bias in Natural Language Processing: Literature Review
Tony Sun | Andrew Gaut | Shirlyn Tang | Yuxin Huang | Mai ElSherief | Jieyu Zhao | Diba Mirza | Elizabeth Belding | Kai-Wei Chang | William Yang Wang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

As Natural Language Processing (NLP) and Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases and stereotypes. Although NLP models have shown success in modeling various applications, they propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to mitigate gender bias in NLP are relatively nascent. In this paper, we review contemporary studies on recognizing and mitigating gender bias in NLP. We discuss gender bias based on four forms of representation bias and analyze methods recognizing gender bias. Furthermore, we discuss the advantages and drawbacks of existing gender debiasing methods. Finally, we discuss future studies for recognizing and mitigating gender bias in NLP.

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Few-Shot Representation Learning for Out-Of-Vocabulary Words
Ziniu Hu | Ting Chen | Kai-Wei Chang | Yizhou Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Existing approaches for learning word embedding often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately estimated from their contexts. However, in real-world scenarios, out-of-vocabulary (a.k.a. OOV) words that do not appear in training corpus emerge frequently. How to learn accurate representations of these words to augment a pre-trained embedding by only a few observations is a challenging research problem. In this paper, we formulate the learning of OOV embedding as a few-shot regression problem by fitting a representation function to predict an oracle embedding vector (defined as embedding trained with abundant observations) based on limited contexts. Specifically, we propose a novel hierarchical attention network-based embedding framework to serve as the neural regression function, in which the context information of a word is encoded and aggregated from K observations. Furthermore, we propose to use Model-Agnostic Meta-Learning (MAML) for adapting the learned model to the new corpus fast and robustly. Experiments show that the proposed approach significantly outperforms existing methods in constructing an accurate embedding for OOV words and improves downstream tasks when the embedding is utilized.

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Learning Bilingual Word Embeddings Using Lexical Definitions
Weijia Shi | Muhao Chen | Yingtao Tian | Kai-Wei Chang
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

Bilingual word embeddings, which represent lexicons of different languages in a shared embedding space, are essential for supporting semantic and knowledge transfers in a variety of cross-lingual NLP tasks. Existing approaches to training bilingual word embeddings require either large collections of pre-defined seed lexicons that are expensive to obtain, or parallel sentences that comprise coarse and noisy alignment. In contrast, we propose BiLex that leverages publicly available lexical definitions for bilingual word embedding learning. Without the need of predefined seed lexicons, BiLex comprises a novel word pairing strategy to automatically identify and propagate the precise fine-grain word alignment from lexical definitions. We evaluate BiLex in word-level and sentence-level translation tasks, which seek to find the cross-lingual counterparts of words and sentences respectively. BiLex significantly outperforms previous embedding methods on both tasks.

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Gender Bias in Contextualized Word Embeddings
Jieyu Zhao | Tianlu Wang | Mark Yatskar | Ryan Cotterell | Vicente Ordonez | Kai-Wei Chang
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

In this paper, we quantify, analyze and mitigate gender bias exhibited in ELMo’s contextualized word vectors. First, we conduct several intrinsic analyses and find that (1) training data for ELMo contains significantly more male than female entities, (2) the trained ELMo embeddings systematically encode gender information and (3) ELMo unequally encodes gender information about male and female entities. Then, we show that a state-of-the-art coreference system that depends on ELMo inherits its bias and demonstrates significant bias on the WinoBias probing corpus. Finally, we explore two methods to mitigate such gender bias and show that the bias demonstrated on WinoBias can be eliminated.

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On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing
Wasi Ahmad | Zhisong Zhang | Xuezhe Ma | Eduard Hovy | Kai-Wei Chang | Nanyun Peng
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Different languages might have different word orders. In this paper, we investigate crosslingual transfer and posit that an orderagnostic model will perform better when transferring to distant foreign languages. To test our hypothesis, we train dependency parsers on an English corpus and evaluate their transfer performance on 30 other languages. Specifically, we compare encoders and decoders based on Recurrent Neural Networks (RNNs) and modified self-attentive architectures. The former relies on sequential information while the latter is more flexible at modeling word order. Rigorous experiments and detailed analysis shows that RNN-based architectures transfer well to languages that are close to English, while self-attentive models have better overall cross-lingual transferability and perform especially well on distant languages.

2018

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A Corpus to Learn Refer-to-as Relations for Nominals
Wasi Ahmad | Kai-Wei Chang
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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A Corpus of Drug Usage Guidelines Annotated with Type of Advice
Sarah Masud Preum | Md. Rizwan Parvez | Kai-Wei Chang | John Stankovic
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Learning Word Embeddings for Low-Resource Languages by PU Learning
Chao Jiang | Hsiang-Fu Yu | Cho-Jui Hsieh | Kai-Wei Chang
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Word embedding is a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for learning effective word embedding. However, such a corpus may not be available for some low-resource languages. In this paper, we study how to effectively learn a word embedding model on a corpus with only a few million tokens. In such a situation, the co-occurrence matrix is sparse as the co-occurrences of many word pairs are unobserved. In contrast to existing approaches often only sample a few unobserved word pairs as negative samples, we argue that the zero entries in the co-occurrence matrix also provide valuable information. We then design a Positive-Unlabeled Learning (PU-Learning) approach to factorize the co-occurrence matrix and validate the proposed approaches in four different languages.

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Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
Jieyu Zhao | Tianlu Wang | Mark Yatskar | Vicente Ordonez | Kai-Wei Chang
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

In this paper, we introduce a new benchmark for co-reference resolution focused on gender bias, WinoBias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing datasets.

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Generating Natural Language Adversarial Examples
Moustafa Alzantot | Yash Sharma | Ahmed Elgohary | Bo-Jhang Ho | Mani Srivastava | Kai-Wei Chang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify. In the image domain, these perturbations can often be made virtually indistinguishable to human perception, causing humans and state-of-the-art models to disagree. However, in the natural language domain, small perturbations are clearly perceptible, and the replacement of a single word can drastically alter the semantics of the document. Given these challenges, we use a black-box population-based optimization algorithm to generate semantically and syntactically similar adversarial examples that fool well-trained sentiment analysis and textual entailment models with success rates of 97% and 70%, respectively. We additionally demonstrate that 92.3% of the successful sentiment analysis adversarial examples are classified to their original label by 20 human annotators, and that the examples are perceptibly quite similar. Finally, we discuss an attempt to use adversarial training as a defense, but fail to yield improvement, demonstrating the strength and diversity of our adversarial examples. We hope our findings encourage researchers to pursue improving the robustness of DNNs in the natural language domain.

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Learning Gender-Neutral Word Embeddings
Jieyu Zhao | Yichao Zhou | Zeyu Li | Wei Wang | Kai-Wei Chang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Word embedding models have become a fundamental component in a wide range of Natural Language Processing (NLP) applications. However, embeddings trained on human-generated corpora have been demonstrated to inherit strong gender stereotypes that reflect social constructs. To address this concern, in this paper, we propose a novel training procedure for learning gender-neutral word embeddings. Our approach aims to preserve gender information in certain dimensions of word vectors while compelling other dimensions to be free of gender influence. Based on the proposed method, we generate a Gender-Neutral variant of GloVe (GN-GloVe). Quantitative and qualitative experiments demonstrate that GN-GloVe successfully isolates gender information without sacrificing the functionality of the embedding model.

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Building Language Models for Text with Named Entities
Md Rizwan Parvez | Saikat Chakraborty | Baishakhi Ray | Kai-Wei Chang
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Text in many domains involves a significant amount of named entities. Predicting the entity names is often challenging for a language model as they appear less frequent on the training corpus. In this paper, we propose a novel and effective approach to building a language model which can learn the entity names by leveraging their entity type information. We also introduce two benchmark datasets based on recipes and Java programming codes, on which we evaluate the proposed model. Experimental results show that our model achieves 52.2% better perplexity in recipe generation and 22.06% on code generation than state-of-the-art language models.

2017

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Counterfactual Language Model Adaptation for Suggesting Phrases
Kenneth Arnold | Kai-Wei Chang | Adam Kalai
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Mobile devices use language models to suggest words and phrases for use in text entry. Traditional language models are based on contextual word frequency in a static corpus of text. However, certain types of phrases, when offered to writers as suggestions, may be systematically chosen more often than their frequency would predict. In this paper, we propose the task of generating suggestions that writers accept, a related but distinct task to making accurate predictions. Although this task is fundamentally interactive, we propose a counterfactual setting that permits offline training and evaluation. We find that even a simple language model can capture text characteristics that improve acceptability.

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Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context
Shyam Upadhyay | Kai-Wei Chang | Matt Taddy | Adam Kalai | James Zou
Proceedings of the 2nd Workshop on Representation Learning for NLP

Word embeddings, which represent a word as a point in a vector space, have become ubiquitous to several NLP tasks. A recent line of work uses bilingual (two languages) corpora to learn a different vector for each sense of a word, by exploiting crosslingual signals to aid sense identification. We present a multi-view Bayesian non-parametric algorithm which improves multi-sense wor d embeddings by (a) using multilingual (i.e., more than two languages) corpora to significantly improve sense embeddings beyond what one achieves with bilingual information, and (b) uses a principled approach to learn a variable number of senses per word, in a data-driven manner. Ours is the first approach with the ability to leverage multilingual corpora efficiently for multi-sense representation learning. Experiments show that multilingual training significantly improves performance over monolingual and bilingual training, by allowing us to combine different parallel corpora to leverage multilingual context. Multilingual training yields comparable performance to a state of the art monolingual model trained on five times more training data.

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Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing
Kai-Wei Chang | Ming-Wei Chang | Vivek Srikumar | Alexander M. Rush
Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing

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Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Jieyu Zhao | Tianlu Wang | Mark Yatskar | Vicente Ordonez | Kai-Wei Chang
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Language is increasingly being used to de-fine rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occurring labels and visual input but risk inadvertently encoding social biases found in web corpora. In this work, we study data and models associated with multilabel object classification and visual semantic role labeling. We find that (a) datasets for these tasks contain significant gender bias and (b) models trained on these datasets further amplify existing bias. For example, the activity cooking is over 33% more likely to involve females than males in a training set, and a trained model further amplifies the disparity to 68% at test time. We propose to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for collective inference. Our method results in almost no performance loss for the underlying recognition task but decreases the magnitude of bias amplification by 47.5% and 40.5% for multilabel classification and visual semantic role labeling, respectively。

2016

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Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems
Shyam Upadhyay | Ming-Wei Chang | Kai-Wei Chang | Wen-tau Yih
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the Workshop on Structured Prediction for NLP
Kai-Wei Chang | Ming-Wei Chang | Alexander Rush | Vivek Srikumar
Proceedings of the Workshop on Structured Prediction for NLP

2015

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A Joint Framework for Coreference Resolution and Mention Head Detection
Haoruo Peng | Kai-Wei Chang | Dan Roth
Proceedings of the Nineteenth Conference on Computational Natural Language Learning

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Hands-on Learning to Search for Structured Prediction
Hal Daumé III | John Langford | Kai-Wei Chang | He He | Sudha Rao
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts

2014

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The Illinois-Columbia System in the CoNLL-2014 Shared Task
Alla Rozovskaya | Kai-Wei Chang | Mark Sammons | Dan Roth | Nizar Habash
Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task

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Typed Tensor Decomposition of Knowledge Bases for Relation Extraction
Kai-Wei Chang | Wen-tau Yih | Bishan Yang | Christopher Meek
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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A Constrained Latent Variable Model for Coreference Resolution
Kai-Wei Chang | Rajhans Samdani | Dan Roth
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Multi-Relational Latent Semantic Analysis
Kai-Wei Chang | Wen-tau Yih | Christopher Meek
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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The University of Illinois System in the CoNLL-2013 Shared Task
Alla Rozovskaya | Kai-Wei Chang | Mark Sammons | Dan Roth
Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task

2012

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Illinois-Coref: The UI System in the CoNLL-2012 Shared Task
Kai-Wei Chang | Rajhans Samdani | Alla Rozovskaya | Mark Sammons | Dan Roth
Joint Conference on EMNLP and CoNLL - Shared Task

2011

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Inference Protocols for Coreference Resolution
Kai-Wei Chang | Rajhans Samdani | Alla Rozovskaya | Nick Rizzolo | Mark Sammons | Dan Roth
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task

2009

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Iterative Scaling and Coordinate Descent Methods for Maximum Entropy
Fang-Lan Huang | Cho-Jui Hsieh | Kai-Wei Chang | Chih-Jen Lin
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers