Jin Qian


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Capturing Conversational Interaction for Question Answering via Global History Reasoning
Jin Qian | Bowei Zou | Mengxing Dong | Xiao Li | AiTi Aw | Yu Hong
Findings of the Association for Computational Linguistics: NAACL 2022

Conversational Question Answering (ConvQA) is required to answer the current question, conditioned on the observable paragraph-level context and conversation history. Previous works have intensively studied history-dependent reasoning. They perceive and absorb topic-related information of prior utterances in the interactive encoding stage. It yielded significant improvement compared to history-independent reasoning. This paper further strengthens the ConvQA encoder by establishing long-distance dependency among global utterances in multi-turn conversation. We use multi-layer transformers to resolve long-distance relationships, which potentially contribute to the reweighting of attentive information in historical utterances. Experiments on QuAC show that our method obtains a substantial improvement (1%), yielding the F1 score of 73.7%. All source codes are available at https://github.com/jaytsien/GHR.


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基于多任务学习的生成式阅读理解(Generative Reading Comprehension via Multi-task Learning)
Jin Qian (钱锦) | Rongtao Huang (黄荣涛) | Bowei Zou (邹博伟) | Yu Hong (洪宇)
Proceedings of the 19th Chinese National Conference on Computational Linguistics



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Generating Abbreviations for Chinese Named Entities Using Recurrent Neural Network with Dynamic Dictionary
Qi Zhang | Jin Qian | Ya Guo | Yaqian Zhou | Xuanjing Huang
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


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Discourse Level Explanatory Relation Extraction from Product Reviews Using First-Order Logic
Qi Zhang | Jin Qian | Huan Chen | Jihua Kang | Xuanjing Huang
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Chinese Named Entity Abbreviation Generation Using First-Order Logic
Huan Chen | Qi Zhang | Jin Qian | Xuanjing Huang
Proceedings of the Sixth International Joint Conference on Natural Language Processing