Leveraging Query Resolution and Reading Comprehension for Conversational Passage Retrieval

• URL: http://arxiv.org/abs/2102.08795v1
• Date: Wed, 17 Feb 2021 14:41:57 GMT
• Title: Leveraging Query Resolution and Reading Comprehension for Conversational Passage Retrieval
• Authors: Svitlana Vakulenko, Nikos Voskarides, Zhucheng Tu, Shayne Longpre
• Abstract summary: This paper describes the participation of UvA.ILPS group at the TREC CAsT 2020 track. Our pipeline consists of (i) an initial retrieval module that uses BM25, and (ii) a re-ranking module that combines the score of a BERT ranking model with the score of a machine comprehension model adjusted for passage retrieval.
• Score: 6.490148466525755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper describes the participation of UvA.ILPS group at the TREC CAsT 2020 track. Our passage retrieval pipeline consists of (i) an initial retrieval module that uses BM25, and (ii) a re-ranking module that combines the score of a BERT ranking model with the score of a machine comprehension model adjusted for passage retrieval. An important challenge in conversational passage retrieval is that queries are often under-specified. Thus, we perform query resolution, that is, add missing context from the conversation history to the current turn query using QuReTeC, a term classification query resolution model. We show that our best automatic and manual runs outperform the corresponding median runs by a large margin.

Related papers

• Sequencing Matters: A Generate-Retrieve-Generate Model for Building Conversational Agents [9.191944519634111]
  The Georgetown InfoSense group has done in regard to solving the challenges presented by TREC iKAT 2023. Our submitted runs outperform the median runs by a significant margin, exhibiting superior performance in nDCG across various cut numbers and in overall success rate. Our solution involves the use of Large Language Models (LLMs) for initial answers, answer grounding by BM25, passage quality filtering by logistic regression, and answer generation by LLMs again.
  arXiv  Detail & Related papers  (2023-11-16T02:37:58Z)
• End-to-end Knowledge Retrieval with Multi-modal Queries [50.01264794081951]
  ReMuQ requires a system to retrieve knowledge from a large corpus by integrating contents from both text and image queries. We introduce a retriever model ReViz'' that can directly process input text and images to retrieve relevant knowledge in an end-to-end fashion. We demonstrate superior performance in retrieval on two datasets under zero-shot settings.
  arXiv  Detail & Related papers  (2023-06-01T08:04:12Z)
• Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking [56.80065604034095]
  We introduce a kNN approach that re-ranks documents based on their similarity with the query and the documents the user considers relevant. To evaluate our different integration strategies, we transform four existing information retrieval datasets into the relevance feedback scenario.
  arXiv  Detail & Related papers  (2022-10-19T16:19:37Z)
• IITD-DBAI: Multi-Stage Retrieval with Pseudo-Relevance Feedback and Query Reformulation [0.0]
  Resolving the contextual dependency is one of the most challenging tasks in the Conversational system. Our submission has produced a mean NDCG@3 performance better than the median model.
  arXiv  Detail & Related papers  (2022-03-31T14:07:47Z)
• Meeting Summarization with Pre-training and Clustering Methods [6.47783315109491]
  HMNetcitehmnet is a hierarchical network that employs both a word-level transformer and a turn-level transformer, as the baseline. We extend the locate-then-summarize approach of QMSumciteqmsum with an intermediate clustering step. We compare the performance of our baseline models with BART, a state-of-the-art language model that is effective for summarization.
  arXiv  Detail & Related papers  (2021-11-16T03:14:40Z)
• 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)
• Brown University at TREC Deep Learning 2019 [11.63256359906015]
  This paper describes Brown University's submission to the TREC 2019 Deep Learning track. Brown's team ranked 3rd in the passage retrieval task (including full ranking and re-ranking), and 2nd when considering only re-ranking submissions.
  arXiv  Detail & Related papers  (2020-09-08T22:54:03Z)
• 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)
• Query Focused Multi-Document Summarization with Distant Supervision [88.39032981994535]
  Existing work relies heavily on retrieval-style methods for estimating the relevance between queries and text segments. We propose a coarse-to-fine modeling framework which introduces separate modules for estimating whether segments are relevant to the query. We demonstrate that our framework outperforms strong comparison systems on standard QFS benchmarks.
  arXiv  Detail & Related papers  (2020-04-06T22:35:19Z)
• Pre-training Tasks for Embedding-based Large-scale Retrieval [68.01167604281578]
  We consider the large-scale query-document retrieval problem. Given a query (e.g., a question), return the set of relevant documents from a large document corpus. We show that the key ingredient of learning a strong embedding-based Transformer model is the set of pre-training tasks.
  arXiv  Detail & Related papers  (2020-02-10T16:44:00Z)

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.