PAQA: Toward ProActive Open-Retrieval Question Answering

• URL: http://arxiv.org/abs/2402.16608v1
• Date: Mon, 26 Feb 2024 14:40:34 GMT
• Title: PAQA: Toward ProActive Open-Retrieval Question Answering
• Authors: Pierre Erbacher and Jian-Yun Nie and Philippe Preux and Laure Soulier
• Abstract summary: This work aims to tackle the challenge of generating relevant clarifying questions by taking into account the inherent ambiguities present in both user queries and documents. We propose PAQA, an extension to the existing AmbiNQ dataset, incorporating clarifying questions. We then evaluate various models and assess how passage retrieval impacts ambiguity detection and the generation of clarifying questions.
• Score: 34.883834970415734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conversational systems have made significant progress in generating natural language responses. However, their potential as conversational search systems is currently limited due to their passive role in the information-seeking process. One major limitation is the scarcity of datasets that provide labelled ambiguous questions along with a supporting corpus of documents and relevant clarifying questions. This work aims to tackle the challenge of generating relevant clarifying questions by taking into account the inherent ambiguities present in both user queries and documents. To achieve this, we propose PAQA, an extension to the existing AmbiNQ dataset, incorporating clarifying questions. We then evaluate various models and assess how passage retrieval impacts ambiguity detection and the generation of clarifying questions. By addressing this gap in conversational search systems, we aim to provide additional supervision to enhance their active participation in the information-seeking process and provide users with more accurate results.

Related papers

• Grounded and Transparent Response Generation for Conversational Information-Seeking Systems [0.0]
  The proposed research delves into the intricacies of response generation in CIS systems. The study focuses on generating responses grounded in the retrieved passages and being transparent about the system's limitations.
  arXiv  Detail & Related papers  (2024-06-27T15:55:25Z)
• CLARINET: Augmenting Language Models to Ask Clarification Questions for Retrieval [52.134133938779776]
  We present CLARINET, a system that asks informative clarification questions by choosing questions whose answers would maximize certainty in the correct candidate. Our approach works by augmenting a large language model (LLM) to condition on a retrieval distribution, finetuning end-to-end to generate the question that would have maximized the rank of the true candidate at each turn.
  arXiv  Detail & Related papers  (2024-04-28T18:21:31Z)
• Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Search [89.1772985740272]
  In mixed-initiative conversational search systems, clarifying questions are used to help users who struggle to express their intentions in a single query. We hypothesize that in scenarios where multimodal information is pertinent, the clarification process can be improved by using non-textual information. We collect a dataset named Melon that contains over 4k multimodal clarifying questions, enriched with over 14k images. Several analyses are conducted to understand the importance of multimodal contents during the query clarification phase.
  arXiv  Detail & Related papers  (2024-02-12T16:04:01Z)
• Towards Reliable and Factual Response Generation: Detecting Unanswerable Questions in Information-Seeking Conversations [16.99952884041096]
  Generative AI models face the challenge of hallucinations that can undermine users' trust in such systems. We approach the problem of conversational information seeking as a two-step process, where relevant passages in a corpus are identified first and then summarized into a final system response. Specifically, our proposed method employs a sentence-level classifier to detect if the answer is present, then aggregates these predictions on the passage level, and eventually across the top-ranked passages to arrive at a final answerability estimate.
  arXiv  Detail & Related papers  (2024-01-21T10:15:36Z)
• Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations [66.16863141262506]
  We present a novel approach that focuses on generating internet search queries guided by social commonsense. Our proposed framework addresses passive user interactions by integrating topic tracking, commonsense response generation and instruction-driven query generation.
  arXiv  Detail & Related papers  (2023-10-22T16:14:56Z)
• 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)
• Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices [0.0]
  The research directions of QA field are analyzed based on the type of question, answer type, source of evidence-answer, and modeling approach. This detailed followed by open challenges of the field like automatic question generation, similarity detection and, low resource availability for a language.
  arXiv  Detail & Related papers  (2021-12-07T08:53:40Z)
• A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answering [52.041815783025186]
  We propose a novel graph-guided retrieval method to model the relations among answers across conversation turns. We also propose to incorporate the multi-round relevance feedback technique to explore the impact of the retrieval context on current question understanding.
  arXiv  Detail & Related papers  (2021-04-17T04:39:41Z)
• Inquisitive Question Generation for High Level Text Comprehension [60.21497846332531]
  We introduce INQUISITIVE, a dataset of 19K questions that are elicited while a person is reading through a document. We show that readers engage in a series of pragmatic strategies to seek information. We evaluate question generation models based on GPT-2 and show that our model is able to generate reasonable questions.
  arXiv  Detail & Related papers  (2020-10-04T19:03:39Z)
• 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)

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.