Papers with generative reader
A Copy-Augmented Generative Model for Open-Domain Question Answering (2022.acl-short)
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| Challenge: | Existing open-domain question answering approaches follow a two-stage paradigm retriever then reader. |
| Approach: | They propose a novel reader-based generative approach that incorporates extractive and generative readers. |
| Outcome: | The proposed model improves on two benchmark datasets, Natural Questions and TriviaQA. |
R2-D2: A Modular Baseline for Open-Domain Question Answering (2021.findings-emnlp)
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| Challenge: | Using extractive and generative reader, we demonstrate its strength across three open-domain QA datasets: NaturalQuestions, TriviaQA and EfficientQA. |
| Approach: | They propose a four-stage open-domain QA pipeline with a retriever, passage reranker, extractive reader, generative reader and a mechanism that aggregates the final prediction from all system’s components. |
| Outcome: | The proposed pipeline outperforms state-of-the-art on three open-domain QA datasets and is twice as effective as the posterior averaging ensemble of the same models with different parameters. |
Answering Open-Domain Multi-Answer Questions via a Recall-then-Verify Framework (2022.acl-long)
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| Challenge: | Existing approaches to open-domain question answering use a rerank-then-read framework . existing approaches use reranked evidence to predict multiple valid answers . |
| Approach: | They propose to use a recall-then-verify framework to solve open-domain questions . the framework separates the reasoning process of each answer to make better use of retrieved evidence . |
| Outcome: | The proposed framework predicts significantly more gold answers on open-domain questions than existing systems that use an oracle reranker. |
Generation-Augmented Retrieval for Open-Domain Question Answering (2021.acl-long)
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| Challenge: | Existing approaches to answer open-domain questions use sparse representations and sparsity. |
| Approach: | They propose a method which augments a query by generating relevant contexts from heuristically discovered contexts without external supervision. |
| Outcome: | The proposed approach outperforms state-of-the-art dense retrieval methods on natural questions and triviaQA datasets. |
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering (2022.acl-long)
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Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, Michael Zeng
| Challenge: | Open-Domain Question Answering (ODQA) models typically include a retrieving module and a reading module. |
| Approach: | They propose a new open-domain question-answering framework that uses a knowledge-enhanced version of FiD to improve the approach. |
| Outcome: | The proposed model improves on ODQA benchmark datasets with less than 40% computation cost. |