Papers with human level performance
Training a Ranking Function for Open-Domain Question Answering (N18-4)
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| Challenge: | Recent advances in machine reading have inspired researchers to combine Information Retrieval with machine reading to tackle open-domain QA. |
| Approach: | They propose two neural network rankers that assign scores to different passages based on their likelihood of containing the answer to a given question. |
| Outcome: | The proposed models achieve human level performance in open-domain QA compared to reading comprehension-style QA because it is difficult to retrieve the pieces of paragraphs that contain the answer to the question. |
proScript: Partially Ordered Scripts Generation (2021.findings-emnlp)
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| Challenge: | Scripts represent structured commonsense knowledge about prototypical events in everyday situations/scenarios such as bake a cake. |
| Approach: | They collect 6.4k crowdsourced partially ordered scripts and develop models that combine language generation and graph structure prediction to generate scripts. |
| Outcome: | The proposed models perform well on two tasks: edge prediction and script generation. |