2021/12/15 by Sihao Chen, Chen, Sihao, Siyi Liu +9 · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Information Retrieval and Search Behavior #Natural Language Processing Techniques #Topic Modeling #cs.CL #cs.IR
paper · pdf · doi:10.48550/arxiv.2112.08357
Findings of NAACL 2022 (Theme Track: Human-Centered Natural Language Processing)
openalex publication_date 2021/12/15 · arxiv created 2022/06/11 · arxiv updated 2022/06/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many users turn to document retrieval systems (e.g. search engines) to seek answers to controversial questions. Answering such user queries usually require identifying responses within web documents, and aggregating the responses based on their different perspectives. Classical document retrieval systems fall short at delivering a set of direct and diverse responses to the users. Naturally, identifying such responses within a document is a natural language understanding task. In this paper, we examine the challenges of synthesizing such language understanding objectives with document retrieval, and study a new perspective-oriented document retrieval paradigm. We discuss and assess the inherent natural language understanding challenges in order to achieve the goal. Following the design challenges and principles, we demonstrate and evaluate a practical prototype pipeline system. We use the prototype system to conduct a user survey in order to assess the utility of our paradigm, as well as understanding the user information needs for controversial queries.