Papers with compositional generalization in
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. |
On Evaluating Multilingual Compositional Generalization with Translated Datasets (2023.acl-long)
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| Challenge: | a growing amount of research investigating compositional generalization in NLP is done on English . a critical semantic distortion is a limitation of the translation of datasets . |
| Approach: | They propose to translate a dataset for evaluating compositional generalization in semantic parsing. |
| Outcome: | The proposed benchmarks show that the translation of the MCWQ dataset suffers from semantic distortion. |
Compositional Generalization with Grounded Language Models (2024.findings-acl)
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| Challenge: | Existing methods for combining language models with knowledge graphs struggle with generalization to sequences of unseen lengths and novel combinations of seen base components. |
| Approach: | They propose a procedure for generating natural language questions paired with knowledge graphs that targets different aspects of compositionality and avoids grounding models in information already encoded in their weights. |
| Outcome: | The proposed method fails to generalize to unseen lengths and to novel combinations of seen base components. |