Chao Wang


2020

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Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling
Xu Cao | Deyi Xiong | Chongyang Shi | Chao Wang | Yao Meng | Changjian Hu
Proceedings of the 28th International Conference on Computational Linguistics

Joint intent detection and slot filling has recently achieved tremendous success in advancing the performance of utterance understanding. However, many joint models still suffer from the robustness problem, especially on noisy inputs or rare/unseen events. To address this issue, we propose a Joint Adversarial Training (JAT) model to improve the robustness of joint intent detection and slot filling, which consists of two parts: (1) automatically generating joint adversarial examples to attack the joint model, and (2) training the model to defend against the joint adversarial examples so as to robustify the model on small perturbations. As the generated joint adversarial examples have different impacts on the intent detection and slot filling loss, we further propose a Balanced Joint Adversarial Training (BJAT) model that applies a balance factor as a regularization term to the final loss function, which yields a stable training procedure. Extensive experiments and analyses on the lightweight models show that our proposed methods achieve significantly higher scores and substantially improve the robustness of both intent detection and slot filling. In addition, the combination of our BJAT with BERT-large achieves state-of-the-art results on two datasets.

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Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning
Hanchu Zhang | Leonhard Hennig | Christoph Alt | Changjian Hu | Yao Meng | Chao Wang
Proceedings of The 3rd Workshop on e-Commerce and NLP

In this work, we introduce a bootstrapped, iterative NER model that integrates a PU learning algorithm for recognizing named entities in a low-resource setting. Our approach combines dictionary-based labeling with syntactically-informed label expansion to efficiently enrich the seed dictionaries. Experimental results on a dataset of manually annotated e-commerce product descriptions demonstrate the effectiveness of the proposed framework.

2019

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Improving Back-Translation with Uncertainty-based Confidence Estimation
Shuo Wang | Yang Liu | Chao Wang | Huanbo Luan | Maosong Sun
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

While back-translation is simple and effective in exploiting abundant monolingual corpora to improve low-resource neural machine translation (NMT), the synthetic bilingual corpora generated by NMT models trained on limited authentic bilingual data are inevitably noisy. In this work, we propose to quantify the confidence of NMT model predictions based on model uncertainty. With word- and sentence-level confidence measures based on uncertainty, it is possible for back-translation to better cope with noise in synthetic bilingual corpora. Experiments on Chinese-English and English-German translation tasks show that uncertainty-based confidence estimation significantly improves the performance of back-translation.

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Multimodal and Multi-view Models for Emotion Recognition
Gustavo Aguilar | Viktor Rozgic | Weiran Wang | Chao Wang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Studies on emotion recognition (ER) show that combining lexical and acoustic information results in more robust and accurate models. The majority of the studies focus on settings where both modalities are available in training and evaluation. However, in practice, this is not always the case; getting ASR output may represent a bottleneck in a deployment pipeline due to computational complexity or privacy-related constraints. To address this challenge, we study the problem of efficiently combining acoustic and lexical modalities during training while still providing a deployable acoustic model that does not require lexical inputs. We first experiment with multimodal models and two attention mechanisms to assess the extent of the benefits that lexical information can provide. Then, we frame the task as a multi-view learning problem to induce semantic information from a multimodal model into our acoustic-only network using a contrastive loss function. Our multimodal model outperforms the previous state of the art on the USC-IEMOCAP dataset reported on lexical and acoustic information. Additionally, our multi-view-trained acoustic network significantly surpasses models that have been exclusively trained with acoustic features.

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Explicit Utilization of General Knowledge in Machine Reading Comprehension
Chao Wang | Hui Jiang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

To bridge the gap between Machine Reading Comprehension (MRC) models and human beings, which is mainly reflected in the hunger for data and the robustness to noise, in this paper, we explore how to integrate the neural networks of MRC models with the general knowledge of human beings. On the one hand, we propose a data enrichment method, which uses WordNet to extract inter-word semantic connections as general knowledge from each given passage-question pair. On the other hand, we propose an end-to-end MRC model named as Knowledge Aided Reader (KAR), which explicitly uses the above extracted general knowledge to assist its attention mechanisms. Based on the data enrichment method, KAR is comparable in performance with the state-of-the-art MRC models, and significantly more robust to noise than them. When only a subset (20%-80%) of the training examples are available, KAR outperforms the state-of-the-art MRC models by a large margin, and is still reasonably robust to noise.

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The Lower The Simpler: Simplifying Hierarchical Recurrent Models
Chao Wang | Hui Jiang
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)

To improve the training efficiency of hierarchical recurrent models without compromising their performance, we propose a strategy named as “the lower the simpler”, which is to simplify the baseline models by making the lower layers simpler than the upper layers. We carry out this strategy to simplify two typical hierarchical recurrent models, namely Hierarchical Recurrent Encoder-Decoder (HRED) and R-NET, whose basic building block is GRU. Specifically, we propose Scalar Gated Unit (SGU), which is a simplified variant of GRU, and use it to replace the GRUs at the middle layers of HRED and R-NET. Besides, we also use Fixed-size Ordinally-Forgetting Encoding (FOFE), which is an efficient encoding method without any trainable parameter, to replace the GRUs at the bottom layers of HRED and R-NET. The experimental results show that the simplified HRED and the simplified R-NET contain significantly less trainable parameters, consume significantly less training time, and achieve slightly better performance than their baseline models.

2008

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A Multimodal Home Entertainment Interface via a Mobile Device
Alexander Gruenstein | Bo-June Paul Hsu | James Glass | Stephanie Seneff | Lee Hetherington | Scott Cyphers | Ibrahim Badr | Chao Wang | Sean Liu
Proceedings of the ACL-08: HLT Workshop on Mobile Language Processing

2007

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Automatic Assessment of Student Translations for Foreign Language Tutoring
Chao Wang | Stephanie Seneff
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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Spoken Dialogue Systems for Language Learning
Stephanie Seneff | Chao Wang | Chih-yu Chao
Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT)

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Chinese Syntactic Reordering for Statistical Machine Translation
Chao Wang | Michael Collins | Philipp Koehn
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2005

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Automatic Induction of Language Model Data for A Spoken Dialogue System
Grace Chung | Stephanie Seneff | Chao Wang
Proceedings of the 6th SIGdial Workshop on Discourse and Dialogue

2004

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Analysis and Processing of Lecture Audio Data: Preliminary Investigations
James Glass | Timothy J. Hazen | Lee Hetherington | Chao Wang
Proceedings of the Workshop on Interdisciplinary Approaches to Speech Indexing and Retrieval at HLT-NAACL 2004

2003

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Automatic Acquisition of Names Using Speak and Spell Mode in Spoken Dialogue Systems
Grace Chung | Stephanie Seneff | Chao Wang
Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics