Compositional Generalisation with Structured Reordering and Fertility Layers (2023.eacl-main)
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| Challenge: | Seq2seq models struggle with compositional generalisation, i.e. generalising to new and potentially more complex structures than seen during training. |
| Approach: | They propose a flexible end-to-end differentiable neural model that composes two structural operations: a fertility step and a reordering step. |
| Outcome: | The proposed model outperforms seq2seq models on compositional splits of realistic semantic parsing tasks. |
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| Challenge: | Existing approaches to semantic parsing only evaluated on synthetic datasets that are not representative of natural language variation. |
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| Challenge: | Recent work on semantic parsing has shown that seq2seq models find compositional generalization challenging. |
| Approach: | They propose a data-augmentation strategy that exploits alignment annotations between sentences and their corresponding meaning representations to improve compositional generalization. |
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Compositional Generalization by Factorizing Alignment and Translation (2020.acl-srw)
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| Challenge: | a crucial property underlying the expressive power of human language is its systematicity. |
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| Challenge: | Generic unstructured neural networks struggle on out-of-distribution compositional generalization. |
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Translate First Reorder Later: Leveraging Monotonicity in Semantic Parsing (2023.findings-eacl)
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| Challenge: | Existing approaches that model alignments between sentences fail at compositional generalization tasks, resulting in a resurgence of such approaches. |
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Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization (2021.emnlp-main)
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| Challenge: | Recent research shows that automatic generation of synthetic utterance-program pairs can alleviate the first problem, but its potential for the second has thus far been under-explored. |
| Approach: | They propose to generate synthetic utterance-program pairs for improving compositional generalization in semantic parsing by using structurally-diverse examples. |
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