Chenglei Si


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Adversarial Training for Machine Reading Comprehension with Virtual Embeddings
Ziqing Yang | Yiming Cui | Chenglei Si | Wanxiang Che | Ting Liu | Shijin Wang | Guoping Hu
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

Adversarial training (AT) as a regularization method has proved its effectiveness on various tasks. Though there are successful applications of AT on some NLP tasks, the distinguishing characteristics of NLP tasks have not been exploited. In this paper, we aim to apply AT on machine reading comprehension (MRC) tasks. Furthermore, we adapt AT for MRC tasks by proposing a novel adversarial training method called PQAT that perturbs the embedding matrix instead of word vectors. To differentiate the roles of passages and questions, PQAT uses additional virtual P/Q-embedding matrices to gather the global perturbations of words from passages and questions separately. We test the method on a wide range of MRC tasks, including span-based extractive RC and multiple-choice RC. The results show that adversarial training is effective universally, and PQAT further improves the performance.

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What’s in a Name? Answer Equivalence For Open-Domain Question Answering
Chenglei Si | Chen Zhao | Jordan Boyd-Graber
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

A flaw in QA evaluation is that annotations often only provide one gold answer. Thus, model predictions semantically equivalent to the answer but superficially different are considered incorrect. This work explores mining alias entities from knowledge bases and using them as additional gold answers (i.e., equivalent answers). We incorporate answers for two settings: evaluation with additional answers and model training with equivalent answers. We analyse three QA benchmarks: Natural Questions, TriviaQA, and SQuAD. Answer expansion increases the exact match score on all datasets for evaluation, while incorporating it helps model training over real-world datasets. We ensure the additional answers are valid through a human post hoc evaluation.

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Benchmarking Robustness of Machine Reading Comprehension Models
Chenglei Si | Ziqing Yang | Yiming Cui | Wentao Ma | Ting Liu | Shijin Wang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning
Chenglei Si | Zhengyan Zhang | Fanchao Qi | Zhiyuan Liu | Yasheng Wang | Qun Liu | Maosong Sun
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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CharBERT: Character-aware Pre-trained Language Model
Wentao Ma | Yiming Cui | Chenglei Si | Ting Liu | Shijin Wang | Guoping Hu
Proceedings of the 28th International Conference on Computational Linguistics

Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almost avoidable. However, those methods split a word into subword units and make the representation incomplete and fragile.In this paper, we propose a character-aware pre-trained language model named CharBERT improving on the previous methods (such as BERT, RoBERTa) to tackle these problems. We first construct the contextual word embedding for each token from the sequential character representations, then fuse the representations of characters and the subword representations by a novel heterogeneous interaction module. We also propose a new pre-training task named NLM (Noisy LM) for unsupervised character representation learning. We evaluate our method on question answering, sequence labeling, and text classification tasks, both on the original datasets and adversarial misspelling test sets. The experimental results show that our method can significantly improve the performance and robustness of PLMs simultaneously.


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Dataset Mention Extraction and Classification
Animesh Prasad | Chenglei Si | Min-Yen Kan
Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications

Datasets are integral artifacts of empirical scientific research. However, due to natural language variation, their recognition can be difficult and even when identified, can often be inconsistently referred across and within publications. We report our approach to the Coleridge Initiative’s Rich Context Competition, which tasks participants with identifying dataset surface forms (dataset mention extraction) and associating the extracted mention to its referred dataset (dataset classification). In this work, we propose various neural baselines and evaluate these model on one-plus and zero-shot classification scenarios. We further explore various joint learning approaches - exploring the synergy between the tasks - and report the issues with such techniques.

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Sentiment Aware Neural Machine Translation
Chenglei Si | Kui Wu | Ai Ti Aw | Min-Yen Kan
Proceedings of the 6th Workshop on Asian Translation

Sentiment ambiguous lexicons refer to words where their polarity depends strongly on con- text. As such, when the context is absent, their translations or their embedded sentence ends up (incorrectly) being dependent on the training data. While neural machine translation (NMT) has achieved great progress in recent years, most systems aim to produce one single correct translation for a given source sentence. We investigate the translation variation in two sentiment scenarios. We perform experiments to study the preservation of sentiment during translation with three different methods that we propose. We conducted tests with both sentiment and non-sentiment bearing contexts to examine the effectiveness of our methods. We show that NMT can generate both positive- and negative-valent translations of a source sentence, based on a given input sentiment label. Empirical evaluations show that our valence-sensitive embedding (VSE) method significantly outperforms a sequence-to-sequence (seq2seq) baseline, both in terms of BLEU score and ambiguous word translation accuracy in test, given non-sentiment bearing contexts.