Papers with retrieval-based Question Answering
Question-Answer Sentence Graph for Joint Modeling Answer Selection (2023.eacl-main)
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| Challenge: | Existing approaches to automate Question Answering (QA) are graph-based and can target large text databases. |
| Approach: | They propose graph-based approaches for Answer Sentence Selection (AS2) . they train and integrate state-of-the-art (SOTA) models for computing scores . |
| Outcome: | The proposed approach outperforms baseline models on academic benchmarks and a real-world dataset on unseen queries. |
Joint Models for Answer Verification in Question Answering Systems (2021.acl-long)
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| Challenge: | Using a joint approach, we found that the model is more efficient than those developed in machine reading (MR) work. |
| Approach: | They propose a joint model for selecting correct answer sentences among the top k provided by answer sentence selection modules. |
| Outcome: | The proposed model improves on WikiQA, TREC-QA, and a real-world dataset. |
Reference-based Weak Supervision for Answer Sentence Selection using Web Data (2021.findings-emnlp)
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| Challenge: | Existing solutions for QA use weakly-supervised data from Web. |
| Approach: | They propose a data pipeline that harvests weakly-supervised answer sentences from Web data . they train TANDA models, which are the state of the art for AS2 . |
| Outcome: | The proposed pipeline improves on three different datasets and sets the state-of-the-art models to P@1=90.1% and MAP=92.9%. |
Datasets for Multilingual Answer Sentence Selection (2024.findings-emnlp)
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| Challenge: | Existing datasets for Answer Sentence Selection (AS2) focus on English due to the scarcity of annotated datasets. |
| Approach: | They propose to use a large language model to train answer sentences in English . they use annotated datasets from English and other languages to train AS2 models . |
| Outcome: | The proposed datasets are highly performant and close the performance gap between English and other languages. |
Measuring Retrieval Complexity in Question Answering Systems (2024.findings-acl)
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| Challenge: | a new metric, retrieval complexity (RC), measures the difficulty of answering questions. |
| Approach: | They propose a retrieval complexity metric conditioned on the completeness of retrieved documents . they propose an unsupervised pipeline to measure RC given an arbitrary retrieval system . |
| Outcome: | The proposed pipeline measures RC more accurately than alternative estimators on six challenging QA benchmarks. |