PAQA: Toward ProActive Open-Retrieval Question Answering
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Conversational SearchPassage RetrievalQuestion AnsweringRetrievalSimilar Papers 제목 키워드 기반
CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answering
Recent studies have explored the use of Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) for Knowledge Graph Question Answering (KGQA). They typically require rewriting retrieved subgraphs into natu…
Graph Question AnsweringQuestion AnsweringRAGRetrieval-augmented GenerationEricson: An Interactive Open-Domain Conversational Search Agent
Open-domain conversational search (ODCS) aims to provide valuable, up-to-date information, while maintaining natural conversations to help users refine and ultimately answer information needs. However, creating an effect…
Conversational SearchDialogue ManagementInformation Retrievalintent-classification+4SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval
We introduce SPARTA, a novel neural retrieval method that shows great promise in performance, generalization, and interpretability for open-domain question answering. Unlike many neural ranking methods that use dense vec…
Open-Domain Question AnsweringQuestion AnsweringRetrievalXOR QA: Cross-lingual Open-Retrieval Question Answering
Multilingual question answering tasks typically assume answers exist in the same language as the question. Yet in practice, many languages face both information scarcity -- where languages have few reference articles -- …
ArticlesMachine TranslationQuestion AnsweringRetrieval+1Open-Domain Conversational Question Answering with Historical Answers
Open-domain conversational question answering can be viewed as two tasks: passage retrieval and conversational question answering, where the former relies on selecting candidate passages from a large corpus and the latte…
Conversational Question AnsweringPassage RetrievalQuestion AnsweringRetrieval