Papers by Denis Lukovnikov
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing (2021.findings-emnlp)
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| Challenge: | Neural network models suffer from performance losses when faced with compositionally out-of-distribution data. |
| Approach: | They propose to use neural semantic parsers to detect compositionally out-of-distribution (OOD) data. |
| Outcome: | The proposed methods perform well on the standard SCAN and CFQ datasets. |
Insertion-based Tree Decoding (2021.findings-acl)
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| Challenge: | Existing non-autoregressive decoders that are sub-linear can speed up inference for longer sequences. |
| Approach: | They propose a sub-linear nonautoregressive tree decoder that uses tree-based insertion operations to generate trees in sub-lines . they evaluate their approach on semantic parsing and compare it against strong baselines . |
| Outcome: | The proposed approach achieves competitive accuracies while reducing the number of decoding steps. |