Conversational Search with Mixed-Initiative -- Asking Good Clarification Questions backed-up by Passage Retrieval

• URL: http://arxiv.org/abs/2112.07308v1
• Date: Tue, 14 Dec 2021 11:27:16 GMT
• Title: Conversational Search with Mixed-Initiative -- Asking Good Clarification Questions backed-up by Passage Retrieval
• Authors: Yosi Mass, Doron Cohen, Asaf Yehudai and David Konopnicki
• Abstract summary: We deal with a scenario of conversational search with mixed-initiative: namely user-asks system-answers, as well as system-asks (clarification questions) and user-answers. We focus on the task of selecting the next clarification question, given conversation context. Our method leverages passage retrieval that is used both for an initial selection of relevant candidate clarification questions, as well as for fine-tuning two deep-learning models for re-ranking these candidates.
• Score: 9.078765961879467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We deal with a scenario of conversational search with mixed-initiative: namely user-asks system-answers, as well as system-asks (clarification questions) and user-answers. We focus on the task of selecting the next clarification question, given conversation context. Our method leverages passage retrieval that is used both for an initial selection of relevant candidate clarification questions, as well as for fine-tuning two deep-learning models for re-ranking these candidates. We evaluated our method on two different use-cases. The first is an open domain conversational search in a large web collection. The second is a task-oriented customer-support setup. We show that our method performs well on both use-cases.

Related papers

• Using LLMs to Investigate Correlations of Conversational Follow-up Queries with User Satisfaction [32.32365329050603]
  We propose a taxonomy of 18 users' follow-up query patterns from conversational search engine Naver Cue:. Compared to the existing literature on query reformulations, we uncovered a new set of motivations and actions behind follow-up queries. Our initial findings suggest some signals of dissatisfactions, such as Clarifying Queries, Excluding Condition, and Substituting Condition with follow-up queries.
  arXiv  Detail & Related papers  (2024-07-18T05:10:35Z)
• Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations [76.70349332096693]
  Information-seeking dialogue systems are widely used in e-commerce systems. We propose a Query-bag based Pseudo Relevance Feedback framework (QB-PRF) It constructs a query-bag with related queries to serve as pseudo signals to guide information-seeking conversations.
  arXiv  Detail & Related papers  (2024-03-22T08:10:32Z)
• Evaluating Mixed-initiative Conversational Search Systems via User Simulation [9.066817876491053]
  We propose a conversational User Simulator, called USi, for automatic evaluation of such search systems. We show that responses generated by USi are both inline with the underlying information need and comparable to human-generated answers.
  arXiv  Detail & Related papers  (2022-04-17T16:27:33Z)
• Soliciting User Preferences in Conversational Recommender Systems via Usage-related Questions [21.184555512370093]
  We propose a novel approach to preference elicitation by asking implicit questions based on item usage. First, we identify the sentences from a large review corpus that contain information about item usage. Then, we generate implicit preference elicitation questions from those sentences using a neural text-to-text model.
  arXiv  Detail & Related papers  (2021-11-26T12:23:14Z)
• Multi-stage Clarification in Conversational AI: The case of Question-Answering Dialogue Systems [0.27998963147546135]
  Clarification resolution plays an important role in various information retrieval tasks such as interactive question answering and conversational search. We propose a multi-stage clarification mechanism for prompting clarification and query selection in the context of a question answering dialogue system. Our proposed mechanism improves the overall user experience and outperforms competitive baselines with two datasets.
  arXiv  Detail & Related papers  (2021-10-28T15:45:44Z)
• Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions [65.60888490988236]
  We release a dataset focused on open-domain single- and multi-turn conversations. We benchmark several state-of-the-art neural baselines. We propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues.
  arXiv  Detail & Related papers  (2021-09-13T09:16:14Z)
• Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots [62.295373408415365]
  We propose a personalized hybrid matching network (PHMN) for context-response matching. Our contributions are two-fold: 1) our model extracts personalized wording behaviors from user-specific dialogue history as extra matching information. We evaluate our model on two large datasets with user identification, i.e., personalized dialogue Corpus Ubuntu (P- Ubuntu) and personalized Weibo dataset (P-Weibo)
  arXiv  Detail & Related papers  (2021-03-17T09:42:11Z)
• ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ) [64.60303062063663]
  This document presents a detailed description of the challenge on clarifying questions for dialogue systems (ClariQ) The challenge is organized as part of the Conversational AI challenge series (ConvAI3) at Search Oriented Conversational AI (SCAI) EMNLP workshop in 2020.
  arXiv  Detail & Related papers  (2020-09-23T19:48:02Z)
• Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search [36.64582291809485]
  Asking clarifying questions in response to ambiguous or faceted queries has been recognized as a useful technique for various information retrieval systems. In this paper, we enrich the representations learned by Transformer networks using a novel attention mechanism from external information sources. Our experiments use a public dataset for search clarification and demonstrate significant improvements compared to competitive baselines.
  arXiv  Detail & Related papers  (2020-06-13T03:24:53Z)
• Open-Retrieval Conversational Question Answering [62.11228261293487]
  We introduce an open-retrieval conversational question answering (ORConvQA) setting, where we learn to retrieve evidence from a large collection before extracting answers. We build an end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader that are all based on Transformers.
  arXiv  Detail & Related papers  (2020-05-22T19:39:50Z)
• Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting [56.268862325167575]
  We tackle conversational passage retrieval (ConvPR) with query reformulation integrated into a multi-stage ad-hoc IR system. We propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, standalone, human-understandable queries with a pretrained sequence-tosequence model.
  arXiv  Detail & Related papers  (2020-05-05T14:30:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.