Jin Qian


2022

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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.

2020

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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

生成式阅读理解是机器阅读理解领域一项新颖且极具挑战性的研究。与主流的抽取式阅读理解相比,生成式阅读理解模型不再局限于从段落中抽取答案,而是能结合问题和段落生成自然和完整的表述作为答案。然而,现有的生成式阅读理解模型缺乏对答案在段落中的边界信息以及对问题类型信息的理解。为解决上述问题,本文提出一种基于多任务学习的生成式阅读理解模型。该模型在训练阶段将答案生成任务作为主任务,答案抽取和问题分类任务作为辅助任务进行多任务学习,同时学习和优化模型编码层参数;在测试阶段加载模型编码层进行解码生成答案。实验结果表明,答案抽取模型和问题分类模型能够有效提升生成式阅读理解模型的性能。

2016

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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

2013

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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