Papers › DyREx: Dynamic Query Representation for Extractive Question Answering
DyREx: Dynamic Query Representation for Extractive Question Answering
Urchade Zaratiana, Niama El Khbir, Dennis Núñez, Pierre Holat, Nadi Tomeh, Thierry Charnois
Extractive question answering (ExQA) is an essential task for Natural Language Processing. The dominant approach to ExQA is one that represents the input sequence tokens (question and passage) with a pre-trained transformer, then uses two learned query vectors to compute distributions over the start and end answer span positions. These query vectors lack the context of the inputs, which can be a bottleneck for the model performance. To address this problem, we propose \textit{DyREx}, a generalization of the \textit{vanilla} approach where we dynamically compute query vectors given the input, using an attention mechanism through transformer layers. Empirical observations demonstrate that our approach consistently improves the performance over the standard one. The code and accompanying files for running the experiments are available at \url{https://github.com/urchade/DyReX}.
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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 | NaturalQA | DyREX | F1 | 78.58 | #4 of 4 | Archive leaderboard | report |
| Question Answering | NewsQA | DyREX | F1 | 68.53 | #18 of 18 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | DyREX | F1 | 91.01 | #212 of 213 | Archive leaderboard | report |
| Question Answering | TriviaQA | DyREX | F1 | 77.37 | #52 of 56 | 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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