Long Context Question Answering via Supervised Contrastive Learning (2022.naacl-main)
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| Challenge: | Long-context question answering tasks often require identifying evidence spans (e.g., sentences) prior work showed that jointly training models to perform evidence extraction and question answering is important for achieving high performance. |
| Approach: | They propose a method for equipping long-context QA models with an additional sequence-level objective for better identification of the supporting evidence. |
| Outcome: | The proposed method exhibits consistent improvements on three different strong long-context transformer models, across two challenging question answering benchmarks – HotpotQA and QAsper. |
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