| Challenge: | Existing work on latent tree learning models shows they do not learn plausible grammars . a dataset is created to study the parsing ability of such models in natural language . |
| Approach: | They propose a toy dataset to study the parsing ability of latent tree learning models . they propose 'listops' toy that has a single correct parse strategy that a system needs to learn . |
| Outcome: | The proposed model outperforms existing models on sentence understanding tasks . it can learn grammars that conform to plausible semantics and syntactic formalisms . |
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| Challenge: | Recent work on latent tree learning attempts to develop models with parse-valued latent variables and train them on non-parsing tasks. |
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Recursive Top-Down Production for Sentence Generation with Latent Trees (2020.findings-emnlp)
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| Challenge: | Various studies have shown that incorporating syntactic structures into recursive encoders can be beneficial for various natural language tasks. |
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Unsupervised Natural Language Parsing (Introductory Tutorial) (2021.eacl-tutorials)
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| Challenge: | Unsupervised parsing learns a syntactic parser from training sentences without parse tree annotations. |
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Latent Structure Models for Natural Language Processing (P19-4)
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| Challenge: | Latent structure models are a powerful tool for compositional data modeling and pipelines. |
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Deep Latent Variable Models of Natural Language (D18-3)
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Learned Incremental Representations for Parsing (2022.acl-long)
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