Papers › Learning Recurrent Span Representations for Extractive Question Answering
Learning Recurrent Span Representations for Extractive Question Answering
Kenton Lee, Shimi Salant, Tom Kwiatkowski, Ankur Parikh, Dipanjan Das, Jonathan Berant
The reading comprehension task, that asks questions about a given evidence document, is a central problem in natural language understanding. Recent formulations of this task have typically focused on answer selection from a set of candidates pre-defined manually or through the use of an external NLP pipeline. However, Rajpurkar et al. (2016) recently released the SQuAD dataset in which the answers can be arbitrary strings from the supplied text. In this paper, we focus on this answer extraction task, presenting a novel model architecture that efficiently builds fixed length representations of all spans in the evidence document with a recurrent network. We show that scoring explicit span representations significantly improves performance over other approaches that factor the prediction into separate predictions about words or start and end markers. Our approach improves upon the best published results of Wang & Jiang (2016) by 5% and decreases the error of Rajpurkar et al.'s baseline by > 50%.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | SQuAD1.1 | RaSoR (single model) | EM | 70.849 | #157 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | RaSoR (single model) | F1 | 78.741 | #157 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | RASOR | EM | 66.4 | #43 of 55 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | RASOR | F1 | 74.9 | #43 of 55 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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