RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Open-domain Question Answering (ODQA) systems rely on spurious features instead of genuine causal relationships to generate answers. |
| Approach: | They propose a model that leverages the encoders of FiD to distinguish between causal relationships and spurious features and guides the decoder to generate answers informed by this discernment. |
| Outcome: | The proposed model improves on two ODQA datasets and shows that it can identify causal relationships and identify spurious features. |
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Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, Michael Zeng
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| Challenge: | Existing approaches to extracting answer from text are expensive to train and train. |
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| Challenge: | Existing open-domain question answering approaches follow a two-stage paradigm retriever then reader. |
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| Challenge: | Recent approaches to Open-domain Question Answering use external knowledge bases, but have separate parameters and are weakly-coupled during training. |
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