Bo Yuan


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Text Editing as Imitation Game
Ning Shi | Bin Tang | Bo Yuan | Longtao Huang | Yewen Pu | Jie Fu | Zhouhan Lin
Findings of the Association for Computational Linguistics: EMNLP 2022

Text editing, such as grammatical error correction, arises naturally from imperfect textual data. Recent works frame text editing as a multi-round sequence tagging task, where operations – such as insertion and substitution – are represented as a sequence of tags. While achieving good results, this encoding is limited in flexibility as all actions are bound to token-level tags. In this work, we reformulate text editing as an imitation game using behavioral cloning. Specifically, we convert conventional sequence-to-sequence data into state-to-action demonstrations, where the action space can be as flexible as needed. Instead of generating the actions one at a time, we introduce a dual decoders structure to parallel the decoding while retaining the dependencies between action tokens, coupled with trajectory augmentation to alleviate the distribution shift that imitation learning often suffers. In experiments on a suite of Arithmetic Equation benchmarks, our model consistently outperforms the autoregressive baselines in terms of performance, efficiency, and robustness. We hope our findings will shed light on future studies in reinforcement learning applying sequence-level action generation to natural language processing.

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Syntax-guided Localized Self-attention by Constituency Syntactic Distance
Shengyuan Hou | Jushi Kai | Haotian Xue | Bingyu Zhu | Bo Yuan | Longtao Huang | Xinbing Wang | Zhouhan Lin
Findings of the Association for Computational Linguistics: EMNLP 2022

Recent works have revealed that Transformers are implicitly learning the syntactic information in its lower layers from data, albeit is highly dependent on the quality and scale of the training data. However, learning syntactic information from data is not necessary if we can leverage an external syntactic parser, which provides better parsing quality with well-defined syntactic structures. This could potentially improve Transformer’s performance and sample efficiency. In this work, we propose a syntax-guided localized self-attention for Transformer that allows directly incorporating grammar structures from an external constituency parser. It prohibits the attention mechanism to overweight the grammatically distant tokens over close ones. Experimental results show that our model could consistently improve translation performance on a variety of machine translation datasets, ranging from small to large dataset sizes, and with different source languages.

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RoChBert: Towards Robust BERT Fine-tuning for Chinese
Zihan Zhang | Jinfeng Li | Ning Shi | Bo Yuan | Xiangyu Liu | Rong Zhang | Hui Xue | Donghong Sun | Chao Zhang
Findings of the Association for Computational Linguistics: EMNLP 2022

Despite of the superb performance on a wide range of tasks, pre-trained language models (e.g., BERT) have been proved vulnerable to adversarial texts. In this paper, we present RoChBERT, a framework to build more Robust BERT-based models by utilizing a more comprehensive adversarial graph to fuse Chinese phonetic and glyph features into pre-trained representations during fine-tuning. Inspired by curriculum learning, we further propose to augment the training dataset with adversarial texts in combination with intermediate samples. Extensive experiments demonstrate that RoChBERT outperforms previous methods in significant ways: (i) robust – RoChBERT greatly improves the model robustness without sacrificing accuracy on benign texts. Specifically, the defense lowers the success rates of unlimited and limited attacks by 59.43% and 39.33% respectively, while remaining accuracy of 93.30%; (ii) flexible – RoChBERT can easily extend to various language models to solve different downstream tasks with excellent performance; and (iii) efficient – RoChBERT can be directly applied to the fine-tuning stage without pre-training language model from scratch, and the proposed data augmentation method is also low-cost.


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A Hybrid Model For Grammatical Error Correction
Yang Xiang | Bo Yuan | Yaoyun Zhang | Xiaolong Wang | Wen Zheng | Chongqiang Wei
Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task


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A Mixed Deterministic Model for Coreference Resolution
Bo Yuan | Qingcai Chen | Yang Xiang | Xiaolong Wang | Liping Ge | Zengjian Liu | Meng Liao | Xianbo Si
Joint Conference on EMNLP and CoNLL - Shared Task


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A Cascade Method for Detecting Hedges and their Scope in Natural Language Text
Buzhou Tang | Xiaolong Wang | Xuan Wang | Bo Yuan | Shixi Fan
Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task