Zeqiu Wu


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InSCIt: Information-Seeking Conversations with Mixed-Initiative Interactions
Zeqiu Wu | Ryu Parish | Hao Cheng | Sewon Min | Prithviraj Ammanabrolu | Mari Ostendorf | Hannaneh Hajishirzi
Transactions of the Association for Computational Linguistics, Volume 11

In an information-seeking conversation, a user may ask questions that are under-specified or unanswerable. An ideal agent would interact by initiating different response types according to the available knowledge sources. However, most current studies either fail to or artificially incorporate such agent-side initiative. This work presents InSCIt, a dataset for Information-Seeking Conversations with mixed-initiative Interactions. It contains 4.7K user-agent turns from 805 human-human conversations where the agent searches over Wikipedia and either directly answers, asks for clarification, or provides relevant information to address user queries. The data supports two subtasks, evidence passage identification and response generation, as well as a human evaluation protocol to assess model performance. We report results of two systems based on state-of-the-art models of conversational knowledge identification and open-domain question answering. Both systems significantly underperform humans, suggesting ample room for improvement in future studies.1


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CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
Zeqiu Wu | Yi Luan | Hannah Rashkin | David Reitter | Hannaneh Hajishirzi | Mari Ostendorf | Gaurav Singh Tomar
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each question needs to be interpreted within the dialogue context. Moreover, it can be expensive to re-train well-established retrievers such as search engines that are originally developed for non-conversational queries. To facilitate their use, we develop a query rewriting model CONQRR that rewrites a conversational question in the context into a standalone question. It is trained with a novel reward function to directly optimize towards retrieval using reinforcement learning and can be adapted to any off-the-shelf retriever. CONQRR achieves state-of-the-art results on a recent open-domain CQA dataset containing conversations from three different sources, and is effective for two different off-the-shelf retrievers. Our extensive analysis also shows the robustness of CONQRR to out-of-domain dialogues as well as to zero query rewriting supervision.


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DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization
Zeqiu Wu | Bo-Ru Lu | Hannaneh Hajishirzi | Mari Ostendorf
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Identifying relevant knowledge to be used in conversational systems that are grounded in long documents is critical to effective response generation. We introduce a knowledge identification model that leverages the document structure to provide dialogue-contextualized passage encodings and better locate knowledge relevant to the conversation. An auxiliary loss captures the history of dialogue-document connections. We demonstrate the effectiveness of our model on two document-grounded conversational datasets and provide analyses showing generalization to unseen documents and long dialogue contexts.

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Automatic Document Sketching: Generating Drafts from Analogous Texts
Zeqiu Wu | Michel Galley | Chris Brockett | Yizhe Zhang | Bill Dolan
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Life-iNet: A Structured Network-Based Knowledge Exploration and Analytics System for Life Sciences
Xiang Ren | Jiaming Shen | Meng Qu | Xuan Wang | Zeqiu Wu | Qi Zhu | Meng Jiang | Fangbo Tao | Saurabh Sinha | David Liem | Peipei Ping | Richard Weinshilboum | Jiawei Han
Proceedings of ACL 2017, System Demonstrations