Search Results for author: Bowei Zou

Found 19 papers, 3 papers with code

Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation

no code implementations Findings (EMNLP) 2021 Xin Huang, Jung-jae Kim, Bowei Zou

Complex question answering over knowledge base remains as a challenging task because it involves reasoning over multiple pieces of information, including intermediate entities/relations and other constraints.

Question Answering Text Generation

Capturing Conversational Interaction for Question Answering via Global History Reasoning

1 code implementation Findings (NAACL) 2022 Jin Qian, Bowei Zou, Mengxing Dong, Xiao Li, AiTi Aw, Yu Hong

Conversational Question Answering (ConvQA) is required to answer the current question, conditioned on the observable paragraph-level context and conversation history.

Conversational Question Answering

Automatic True/False Question Generation for Educational Purpose

no code implementations NAACL (BEA) 2022 Bowei Zou, Pengfei Li, Liangming Pan, Ai Ti Aw

In field of teaching, true/false questioning is an important educational method for assessing students’ general understanding of learning materials.

Fact Verification Question Generation +1

汉语否定焦点识别研究:数据集与基线系统(Research on Chinese Negative Focus Identification: Dataset and Baseline)

no code implementations CCL 2020 Jiaxuan Sheng, Bowei Zou, Longxiang Shen, Jing Ye, Yu Hong

自然语言文本中存在大量否定语义表达, 否定焦点识别任务作为更细粒度的否定语义分析, 近年来开始受到自然语言处理学者的关注。该任务旨在识别句子中被否定词修饰和强调的文本片段, 其对自然语言处理的下游任务, 如情感分析、观点挖掘等具有重要意义。与英语相比, 目前面向汉语的否定焦点识别研究彶展缓慢, 其主要原因是尚未有中文数据集为模型提供训练和测试数据。为解决上述问题, 本文在汉语否定与不确定语料库上进行了否定焦点的标注工作, 初步探索了否定焦点在汉语上的语言现象, 并构建了一个包含5, 762个样本的数据集。同时, 本文还提出了一个基于神经网络模型的基线系统, 为后续相关研究提供参照。

基于多任务学习的生成式阅读理解(Generative Reading Comprehension via Multi-task Learning)

no code implementations CCL 2020 Jin Qian, Rongtao Huang, Bowei Zou, Yu Hong

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。

Multi-Task Learning Reading Comprehension

CoHS-CQG: Context and History Selection for Conversational Question Generation

no code implementations14 Sep 2022 Xuan Long Do, Bowei Zou, Liangming Pan, Nancy F. Chen, Shafiq Joty, Ai Ti Aw

While previous studies mainly focus on how to model the flow and alignment of the conversation, there has been no thorough study to date on which parts of the context and history are necessary for the model.

Question Generation Reading Comprehension

Improving Lexical Embeddings for Robust Question Answering

no code implementations28 Feb 2022 Weiwen Xu, Bowei Zou, Wai Lam, Ai Ti Aw

Recent techniques in Question Answering (QA) have gained remarkable performance improvement with some QA models even surpassed human performance.

Question Answering

Multi-grained Chinese Word Segmentation with Weakly Labeled Data

no code implementations COLING 2020 Chen Gong, Zhenghua Li, Bowei Zou, Min Zhang

Detailed evaluation shows that our proposed model with weakly labeled data significantly outperforms the state-of-the-art MWS model by 1. 12 and 5. 97 on NEWS and BAIKE data in F1.

Chinese Word Segmentation

NUT-RC: Noisy User-generated Text-oriented Reading Comprehension

1 code implementation COLING 2020 Rongtao Huang, Bowei Zou, Yu Hong, Wei zhang, AiTi Aw, Guodong Zhou

Most existing RC models are developed on formal datasets such as news articles and Wikipedia documents, which severely limit their performances when directly applied to the noisy and informal texts in social media.

Answer Selection Multi-Task Learning +1

GCDST: A Graph-based and Copy-augmented Multi-domain Dialogue State Tracking

no code implementations Findings of the Association for Computational Linguistics 2020 Peng Wu, Bowei Zou, Ridong Jiang, AiTi Aw

As an essential component of task-oriented dialogue systems, Dialogue State Tracking (DST) takes charge of estimating user intentions and requests in dialogue contexts and extracting substantial goals (states) from user utterances to help the downstream modules to determine the next actions of dialogue systems.

Dialogue State Tracking Multi-domain Dialogue State Tracking +1

Negative Focus Detection via Contextual Attention Mechanism

no code implementations IJCNLP 2019 Longxiang Shen, Bowei Zou, Yu Hong, Guodong Zhou, Qiaoming Zhu, AiTi Aw

For the sake of understanding a negated statement, it is critical to precisely detect the negative focus in context.

Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition

no code implementations COLING 2018 Yu Hong, Yang Xu, Huibin Ruan, Bowei Zou, Jianmin Yao, Guodong Zhou

In particular, we incorporate image processing into the acquisition of similar event instances, including the utilization of images for visually representing event scenes, and the use of the neural network based image matching for approximate calculation between events.

Adversarial Feature Adaptation for Cross-lingual Relation Classification

1 code implementation COLING 2018 Bowei Zou, Zengzhuang Xu, Yu Hong, Guodong Zhou

In this paper, we come up with a feature adaptation approach for cross-lingual relation classification, which employs a generative adversarial network (GAN) to transfer feature representations from one language with rich annotated data to another language with scarce annotated data.

Classification Domain Adaptation +5

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