Papers with parsing strategy
ListOps: A Diagnostic Dataset for Latent Tree Learning (N18-4)
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| 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 . |
Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic Oracle (N19-1)
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| Challenge: | Discontinuous constituency trees are derivations of Linear Context-Free Rewriting Systems (LCFRS), which makes them much harder to parse. |
| Approach: | They propose a transition system that uses a set of parsing items with constant-time random access instead of storing subtrees in a stack . |
| Outcome: | The proposed system constructs a discontinuous constituency tree in 4n–2 transitions for a sentence of length n. |
Cooperative Learning of Disjoint Syntax and Semantics (N19-1)
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| Challenge: | Existing models that learn to jointly infer an expression’s syntactic structure and its semantics fail to learn the correct parsing strategy on mathematical expressions generated from a simple context-free grammar. |
| Approach: | They propose a recursive model that learns to jointly infer an expression’s syntactic structure and its semantics without requiring a formal supervision. |
| Outcome: | The proposed model performs competitively on several natural language tasks, such as Natural Language Inference and Sentiment Analysis. |
Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars (2021.emnlp-main)
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| Challenge: | Existing literature is agnostic about a parsing strategy of hierarchical models . a recent study showed that hierarchically model hierarchic structures capture grammatical dependencies much better than RNNs in targeted syntactic evaluations. |
| Approach: | They evaluated three LMs with head-final left-branching structures and Recurrent Neural Network Grammars with top-down and left-corner parsing strategies as hierarchical models. |
| Outcome: | The proposed model outperforms top-down and left-corner models against human reading times in Japanese. |