Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension (P19-1)
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| Challenge: | Existing approaches to answer reading comprehension tasks are inefficient since the input is re-encoded within each module. |
| Approach: | They propose a unified question answering model that combines context retrieving, reading comprehension, and answer reranking to predict the final answer. |
| Outcome: | The proposed model outperforms the baseline model and achieves state-of-the-art results on two versions of TriviaQA and two variants of SQuAD. |
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| Challenge: | Using extractive and generative reader, we demonstrate its strength across three open-domain QA datasets: NaturalQuestions, TriviaQA and EfficientQA. |
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| Challenge: | Existing methods for multi-document reading comprehension cannot make full of the advantages of both approaches. |
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End-to-End Training of Neural Retrievers for Open-Domain Question Answering (2021.acl-long)
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Devendra Sachan, Mostofa Patwary, Mohammad Shoeybi, Neel Kant, Wei Ping, William L. Hamilton, Bryan Catanzaro
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| Challenge: | Existing methods to train retrieval-based dialogue systems are suboptimal . existing methods to optimize retrieval and rerank modules are sub-optimal, causing sub-optimum performance. |
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Retrieval, Re-ranking and Multi-task Learning for Knowledge-Base Question Answering (2021.eacl-main)
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| Challenge: | Existing work on question answering over knowledge bases limited the search space to a subset of KBs . a retrieval-and-rerank framework is used to access KB and rerank retrieved candidates with more powerful neural networks. |
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