mlsanityData to 7 Oct 2026

Joint Models for Answer Verification in Question Answering Systems

arXiv 2107.04217 ↗First posted 9 Jul 2021cs.CLcited by 13

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Abstract (arXiv)

This paper studies joint models for selecting correct answer sentences among the top k provided by answer sentence selection (AS2) modules, which are core components of retrieval-based Question Answering (QA) systems. Our work shows that a critical step to effectively exploit an answer set regards modeling the interrelated information between pair of answers. For this purpose, we build a three-way multi-classifier, which decides if an answer supports, refutes, or is neutral with respect to another one. More specifically, our neural architecture integrates a state-of-the-art AS2 model with the multi-classifier, and a joint layer connecting all components. We tested our models on WikiQA, TREC-QA, and a real-world dataset. The results show that our models obtain the new state of the art in AS2.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 20 of its 34 references that are in the map. First posted on arXiv 9 Jul 2021.

Builds on

20 of 34 references.

References · 20

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  10. 2019TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selectioncited by 74
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  12. 2019A Compare-Aggregate Model with Latent Clustering for Answer Selectioncited by 19
8 more
  1. 2019SemEval-2019 Task 8: Fact Checking in Community Question Answering Forumscited by 18
  2. 2018Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verificationcited by 17
  3. 2018Adaptive Document Retrieval for Deep Question Answeringcited by 16
  4. 2019Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehensioncited by 15
  5. 2018Fact Checking in Community Forumscited by 10
  6. 2020A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selectioncited by 5
  7. 2019TMLab SRPOL at SemEval-2019 Task 8: Fact Checking in Community Question Answering Forumscited by 2
  8. 2020MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selectioncited by 1

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Citing papers per year: 2022: 5; 2023: 6; 2024: 2.

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  1. 2024CABINET: Content Relevance based Noise Reduction for Table Question Answeringcited by 23
  2. 2024Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratchcited by 20
  3. 2023SmartBook: AI-Assisted Situation Report Generation for Intelligence Analystscited by 9
  4. 2022Paragraph-based Transformer Pre-training for Multi-Sentence Inferencecited by 7
  5. 2022Question-Answer Sentence Graph for Joint Modeling Answer Selectioncited by 6
  6. 2022Knowledge Transfer from Answer Ranking to Answer Generationcited by 6
  7. 2023QUADRo: Dataset and Models for QUestion-Answer Database Retrievalcited by 5
  8. 2022Double Retrieval and Ranking for Accurate Question Answeringcited by 4
  9. 2023Learning Answer Generation using Supervision from Automatic Question Answering Evaluatorscited by 3
  10. 2022In Situ Answer Sentence Selection at Web-scalecited by 2
  11. 2023Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languagescited by 2
  12. 2023SQUARE: Automatic Question Answering Evaluation using Multiple Positive and Negative Referencescited by 1
1 more
  1. 2023Question-Context Alignment and Answer-Context Dependencies for Effective Answer Sentence Selectioncited by 0