Contextualized Sparse Representations for Real-Time Open-Domain Question Answering (2020.acl-main)
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| Challenge: | Existing phrase retrieval models suffer from low accuracy due to limited scalability and speed . 'Open-domain question answering' is a task of answering generic factoid questions by looking up a large knowledge source, typically unstructured text corpora such as Wikipedia. |
| Approach: | They aim to augment existing phrase retrieval models with contextualized sparse representations to improve the quality of each phrase embedding. |
| Outcome: | The proposed model improves CuratedTREC and SQuAD-Open by 4% and 45x faster inference speeds over the existing model. |
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