Meng Zhang


pdf bib
Word-level Textual Adversarial Attacking as Combinatorial Optimization
Yuan Zang | Fanchao Qi | Chenghao Yang | Zhiyuan Liu | Meng Zhang | Qun Liu | Maosong Sun
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Adversarial attacks are carried out to reveal the vulnerability of deep neural networks. Textual adversarial attacking is challenging because text is discrete and a small perturbation can bring significant change to the original input. Word-level attacking, which can be regarded as a combinatorial optimization problem, is a well-studied class of textual attack methods. However, existing word-level attack models are far from perfect, largely because unsuitable search space reduction methods and inefficient optimization algorithms are employed. In this paper, we propose a novel attack model, which incorporates the sememe-based word substitution method and particle swarm optimization-based search algorithm to solve the two problems separately. We conduct exhaustive experiments to evaluate our attack model by attacking BiLSTM and BERT on three benchmark datasets. Experimental results demonstrate that our model consistently achieves much higher attack success rates and crafts more high-quality adversarial examples as compared to baseline methods. Also, further experiments show our model has higher transferability and can bring more robustness enhancement to victim models by adversarial training. All the code and data of this paper can be obtained on


pdf bib
Interpretable Relevant Emotion Ranking with Event-Driven Attention
Yang Yang | Deyu Zhou | Yulan He | Meng Zhang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Multiple emotions with different intensities are often evoked by events described in documents. Oftentimes, such event information is hidden and needs to be discovered from texts. Unveiling the hidden event information can help to understand how the emotions are evoked and provide explainable results. However, existing studies often ignore the latent event information. In this paper, we proposed a novel interpretable relevant emotion ranking model with the event information incorporated into a deep learning architecture using the event-driven attentions. Moreover, corpus-level event embeddings and document-level event distributions are introduced respectively to consider the global events in corpus and the document-specific events simultaneously. Experimental results on three real-world corpora show that the proposed approach performs remarkably better than the state-of-the-art emotion detection approaches and multi-label approaches. Moreover, interpretable results can be obtained to shed light on the events which trigger certain emotions.


pdf bib
Neural Network Methods for Natural Language Processing by Yoav Goldberg
Yang Liu | Meng Zhang
Computational Linguistics, Volume 44, Issue 1 - April 2018

pdf bib
The Effect of Adding Authorship Knowledge in Automated Text Scoring
Meng Zhang | Xie Chen | Ronan Cummins | Øistein E. Andersen | Ted Briscoe
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

Some language exams have multiple writing tasks. When a learner writes multiple texts in a language exam, it is not surprising that the quality of these texts tends to be similar, and the existing automated text scoring (ATS) systems do not explicitly model this similarity. In this paper, we suggest that it could be useful to include the other texts written by this learner in the same exam as extra references in an ATS system. We propose various approaches of fusing information from multiple tasks and pass this authorship knowledge into our ATS model on six different datasets. We show that this can positively affect the model performance at a global level.


pdf bib
Adversarial Training for Unsupervised Bilingual Lexicon Induction
Meng Zhang | Yang Liu | Huanbo Luan | Maosong Sun
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Word embeddings are well known to capture linguistic regularities of the language on which they are trained. Researchers also observe that these regularities can transfer across languages. However, previous endeavors to connect separate monolingual word embeddings typically require cross-lingual signals as supervision, either in the form of parallel corpus or seed lexicon. In this work, we show that such cross-lingual connection can actually be established without any form of supervision. We achieve this end by formulating the problem as a natural adversarial game, and investigating techniques that are crucial to successful training. We carry out evaluation on the unsupervised bilingual lexicon induction task. Even though this task appears intrinsically cross-lingual, we are able to demonstrate encouraging performance without any cross-lingual clues.

pdf bib
Earth Mover’s Distance Minimization for Unsupervised Bilingual Lexicon Induction
Meng Zhang | Yang Liu | Huanbo Luan | Maosong Sun
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Cross-lingual natural language processing hinges on the premise that there exists invariance across languages. At the word level, researchers have identified such invariance in the word embedding semantic spaces of different languages. However, in order to connect the separate spaces, cross-lingual supervision encoded in parallel data is typically required. In this paper, we attempt to establish the cross-lingual connection without relying on any cross-lingual supervision. By viewing word embedding spaces as distributions, we propose to minimize their earth mover’s distance, a measure of divergence between distributions. We demonstrate the success on the unsupervised bilingual lexicon induction task. In addition, we reveal an interesting finding that the earth mover’s distance shows potential as a measure of language difference.


pdf bib
Inducing Bilingual Lexica From Non-Parallel Data With Earth Mover’s Distance Regularization
Meng Zhang | Yang Liu | Huanbo Luan | Yiqun Liu | Maosong Sun
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Being able to induce word translations from non-parallel data is often a prerequisite for cross-lingual processing in resource-scarce languages and domains. Previous endeavors typically simplify this task by imposing the one-to-one translation assumption, which is too strong to hold for natural languages. We remove this constraint by introducing the Earth Mover’s Distance into the training of bilingual word embeddings. In this way, we take advantage of its capability to handle multiple alternative word translations in a natural form of regularization. Our approach shows significant and consistent improvements across four language pairs. We also demonstrate that our approach is particularly preferable in resource-scarce settings as it only requires a minimal seed lexicon.

pdf bib
Constrained Multi-Task Learning for Automated Essay Scoring
Ronan Cummins | Meng Zhang | Ted Briscoe
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


pdf bib
Refining Grammars for Parsing with Hierarchical Semantic Knowledge
Xiaojun Lin | Yang Fan | Meng Zhang | Xihong Wu | Huisheng Chi
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing