Question rewriting? Assessing its importance for conversational question answering

• URL: http://arxiv.org/abs/2201.09146v1
• Date: Sat, 22 Jan 2022 23:31:25 GMT
• Title: Question rewriting? Assessing its importance for conversational question answering
• Authors: Gon\c{c}alo Raposo, Rui Ribeiro, Bruno Martins, and Lu\'isa Coheur
• Abstract summary: This work presents a conversational question answering system designed specifically for the Search-Oriented Conversational AI (SCAI) shared task. In particular, we considered different variations of the question rewriting module to evaluate the influence on the subsequent components. Our system achieved the best performance in the shared task and our analysis emphasizes the importance of the conversation context representation for the overall system performance.
• Score: 0.6449761153631166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In conversational question answering, systems must correctly interpret the interconnected interactions and generate knowledgeable answers, which may require the retrieval of relevant information from a background repository. Recent approaches to this problem leverage neural language models, although different alternatives can be considered in terms of modules for (a) representing user questions in context, (b) retrieving the relevant background information, and (c) generating the answer. This work presents a conversational question answering system designed specifically for the Search-Oriented Conversational AI (SCAI) shared task, and reports on a detailed analysis of its question rewriting module. In particular, we considered different variations of the question rewriting module to evaluate the influence on the subsequent components, and performed a careful analysis of the results obtained with the best system configuration. Our system achieved the best performance in the shared task and our analysis emphasizes the importance of the conversation context representation for the overall system performance.

Related papers

• PICK: Polished & Informed Candidate Scoring for Knowledge-Grounded Dialogue Systems [59.1250765143521]
  Current knowledge-grounded dialogue systems often fail to align the generated responses with human-preferred qualities. We propose Polished & Informed Candidate Scoring (PICK), a generation re-scoring framework. We demonstrate the effectiveness of PICK in generating responses that are more faithful while keeping them relevant to the dialogue history.
  arXiv  Detail & Related papers  (2023-09-19T08:27:09Z)
• FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems [53.89014188309486]
  We present a flexible neural framework that can integrate contextual information from multiple channels. We evaluate our model on the MSDialog dataset widely used for evaluating conversational response ranking tasks.
  arXiv  Detail & Related papers  (2023-03-31T23:58:28Z)
• Interactive Question Answering Systems: Literature Review [17.033640293433397]
  Interactive question answering is a recently proposed and increasingly popular solution that resides at the intersection of question answering and dialogue systems. By permitting the user to ask more questions, interactive question answering enables users to dynamically interact with the system and receive more precise results. This survey offers a detailed overview of the interactive question-answering methods that are prevalent in current literature.
  arXiv  Detail & Related papers  (2022-09-04T13:46:54Z)
• End-to-end Spoken Conversational Question Answering: Task, Dataset and Model [92.18621726802726]
  In spoken question answering, the systems are designed to answer questions from contiguous text spans within the related speech transcripts. We propose a new Spoken Conversational Question Answering task (SCQA), aiming at enabling the systems to model complex dialogue flows. Our main objective is to build the system to deal with conversational questions based on the audio recordings, and to explore the plausibility of providing more cues from different modalities with systems in information gathering.
  arXiv  Detail & Related papers  (2022-04-29T17:56:59Z)
• BERT-CoQAC: BERT-based Conversational Question Answering in Context [10.811729691130349]
  We introduce a framework based on a publically available pre-trained language model called BERT for incorporating history turns into the system. Experiment results revealed that our framework is comparable in performance with the state-of-the-art models on the QuAC leader board.
  arXiv  Detail & Related papers  (2021-04-23T03:05:17Z)
• Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues [88.73739515457116]
  We introduce four self-supervised tasks including next session prediction, utterance restoration, incoherence detection and consistency discrimination. We jointly train the PLM-based response selection model with these auxiliary tasks in a multi-task manner. Experiment results indicate that the proposed auxiliary self-supervised tasks bring significant improvement for multi-turn response selection.
  arXiv  Detail & Related papers  (2020-09-14T08:44:46Z)
• Analysing the Effect of Clarifying Questions on Document Ranking in Conversational Search [10.335808358080289]
  We investigate how different aspects of clarifying questions and user answers affect the quality of ranking. We introduce a simple-based lexical baseline, that significantly outperforms the existing naive baselines.
  arXiv  Detail & Related papers  (2020-08-09T12:55:16Z)
• 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)
• Knowledgeable Dialogue Reading Comprehension on Key Turns [84.1784903043884]
  Multi-choice machine reading comprehension (MRC) requires models to choose the correct answer from candidate options given a passage and a question. Our research focuses dialogue-based MRC, where the passages are multi-turn dialogues. It suffers from two challenges, the answer selection decision is made without support of latently helpful commonsense, and the multi-turn context may hide considerable irrelevant information.
  arXiv  Detail & Related papers  (2020-04-29T07:04:43Z)

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.