Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning

• URL: http://arxiv.org/abs/2306.04293v1
• Date: Wed, 7 Jun 2023 09:46:38 GMT
• Title: Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning
• Authors: Soyeong Jeong, Jinheon Baek, Sung Ju Hwang, Jong C. Park
• Abstract summary: Open-Domain Conversational Question Answering (ODConvQA) aims at answering questions through a multi-turn conversation. We propose a method to directly predict answers with a phrase retrieval scheme for a sequence of words.
• Score: 54.55643652781891
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-Domain Conversational Question Answering (ODConvQA) aims at answering questions through a multi-turn conversation based on a retriever-reader pipeline, which retrieves passages and then predicts answers with them. However, such a pipeline approach not only makes the reader vulnerable to the errors propagated from the retriever, but also demands additional effort to develop both the retriever and the reader, which further makes it slower since they are not runnable in parallel. In this work, we propose a method to directly predict answers with a phrase retrieval scheme for a sequence of words, reducing the conventional two distinct subtasks into a single one. Also, for the first time, we study its capability for ODConvQA tasks. However, simply adopting it is largely problematic, due to the dependencies between previous and current turns in a conversation. To address this problem, we further introduce a novel contrastive learning strategy, making sure to reflect previous turns when retrieving the phrase for the current context, by maximizing representational similarities of consecutive turns in a conversation while minimizing irrelevant conversational contexts. We validate our model on two ODConvQA datasets, whose experimental results show that it substantially outperforms the relevant baselines with the retriever-reader. Code is available at: https://github.com/starsuzi/PRO-ConvQA.

Related papers

• A Multimodal Dense Retrieval Approach for Speech-Based Open-Domain Question Answering [16.613985687431818]
  Passage retrieval is a key task in speech-based open-domain QA. We propose an end-to-end trained multimodal dense retriever that can work directly on spoken questions.
  arXiv  Detail & Related papers  (2024-09-20T13:15:53Z)
• SpeechDPR: End-to-End Spoken Passage Retrieval for Open-Domain Spoken Question Answering [76.4510005602893]
  Spoken Question Answering (SQA) is essential for machines to reply to user's question by finding the answer span within a given spoken passage. This paper proposes the first known end-to-end framework, Speech Passage Retriever (SpeechDPR) SpeechDPR learns a sentence-level semantic representation by distilling knowledge from the cascading model of unsupervised ASR (UASR) and dense text retriever (TDR)
  arXiv  Detail & Related papers  (2024-01-24T14:08:38Z)
• Strong and Efficient Baselines for Open Domain Conversational Question Answering [2.773656427800412]
  We study the State-of-the-Art (SotA) Dense Passage Retrieval (DPR) retriever and Fusion-in-Decoder (FiD) reader pipeline. We propose and evaluate strong yet simple and efficient baselines, by introducing a fast reranking component between the retriever and the reader. Experiments on two ODConvQA tasks, namely TopiOCQA and OR-QuAC, show that our method improves the SotA results, while reducing reader's latency by 60%.
  arXiv  Detail & Related papers  (2023-10-23T08:48:14Z)
• Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading [63.93888816206071]
  We introduce MemWalker, a method that processes the long context into a tree of summary nodes. Upon receiving a query, the model navigates this tree in search of relevant information, and responds once it gathers sufficient information. We show that, beyond effective reading, MemWalker enhances explainability by highlighting the reasoning steps as it interactively reads the text; pinpointing the relevant text segments related to the query.
  arXiv  Detail & Related papers  (2023-10-08T06:18:14Z)
• Open-Domain Conversational Question Answering with Historical Answers [29.756094955426597]
  This paper proposes ConvADR-QA that leverages historical answers to boost retrieval performance. In our proposed framework, the retrievers use a teacher-student framework to reduce noises from previous turns. Our experiments on the benchmark dataset, OR-QuAC, demonstrate that our model outperforms existing baselines in both extractive and generative reader settings.
  arXiv  Detail & Related papers  (2022-11-17T08:20:57Z)
• CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning [16.470428531658232]
  We develop a query rewriting model CONQRR that rewrites a conversational question in context into a standalone question. We show that CONQRR achieves state-of-the-art results on a recent open-domain CQA dataset.
  arXiv  Detail & Related papers  (2021-12-16T01:40:30Z)
• Open Question Answering over Tables and Text [55.8412170633547]
  In open question answering (QA), the answer to a question is produced by retrieving and then analyzing documents that might contain answers to the question. Most open QA systems have considered only retrieving information from unstructured text. We present a new large-scale dataset Open Table-and-Text Question Answering (OTT-QA) to evaluate performance on this task.
  arXiv  Detail & Related papers  (2020-10-20T16:48:14Z)
• Tradeoffs in Sentence Selection Techniques for Open-Domain Question Answering [54.541952928070344]
  We describe two groups of models for sentence selection: QA-based approaches, which run a full-fledged QA system to identify answer candidates, and retrieval-based models, which find parts of each passage specifically related to each question. We show that very lightweight QA models can do well at this task, but retrieval-based models are faster still.
  arXiv  Detail & Related papers  (2020-09-18T23:39:15Z)
• Query Resolution for Conversational Search with Limited Supervision [63.131221660019776]
  We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC.
  arXiv  Detail & Related papers  (2020-05-24T11:37:22Z)
• Conversational Question Answering over Passages by Leveraging Word Proximity Networks [33.59664244897881]
  CROWN is an unsupervised yet effective system for conversational QA with passage responses. It supports several modes of context propagation over multiple turns. CROWN was evaluated on TREC CAsT data, where it achieved above-median performance in a pool of neural methods.
  arXiv  Detail & Related papers  (2020-04-27T19:30:47Z)

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.