Conversations with Search Engines: SERP-based Conversational Response Generation

• URL: http://arxiv.org/abs/2004.14162v2
• Date: Tue, 18 May 2021 06:40:31 GMT
• Title: Conversations with Search Engines: SERP-based Conversational Response Generation
• Authors: Pengjie Ren, Zhumin Chen, Zhaochun Ren, Evangelos Kanoulas, Christof Monz, and Maarten de Rijke
• Abstract summary: We create a suitable dataset, the Search as a Conversation (SaaC) dataset, for the development of pipelines for conversations with search engines. We also develop a state-of-the-art pipeline for conversations with search engines, the Conversations with Search Engines (CaSE) using this dataset. CaSE enhances the state-of-the-art by introducing a supporting token identification module and aprior-aware pointer generator.
• Score: 77.1381159789032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we address the problem of answering complex information needs by conversing conversations with search engines, in the sense that users can express their queries in natural language, and directly receivethe information they need from a short system response in a conversational manner. Recently, there have been some attempts towards a similar goal, e.g., studies on Conversational Agents (CAs) and Conversational Search (CS). However, they either do not address complex information needs, or they are limited to the development of conceptual frameworks and/or laboratory-based user studies. We pursue two goals in this paper: (1) the creation of a suitable dataset, the Search as a Conversation (SaaC) dataset, for the development of pipelines for conversations with search engines, and (2) the development of astate-of-the-art pipeline for conversations with search engines, the Conversations with Search Engines (CaSE), using this dataset. SaaC is built based on a multi-turn conversational search dataset, where we further employ workers from a crowdsourcing platform to summarize each relevant passage into a short, conversational response. CaSE enhances the state-of-the-art by introducing a supporting token identification module and aprior-aware pointer generator, which enables us to generate more accurate responses. We carry out experiments to show that CaSE is able to outperform strong baselines. We also conduct extensive analyses on the SaaC dataset to show where there is room for further improvement beyond CaSE. Finally, we release the SaaC dataset and the code for CaSE and all models used for comparison to facilitate future research on this topic.

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