Papers with unsupervised parsers
Unsupervised Parsing by Searching for Frequent Word Sequences among Sentences with Equivalent Predicate-Argument Structures (2024.findings-acl)
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| Challenge: | Unsupervised constituency parsing focuses on identifying word sequences that form a syntactic unit (i.e., constituents) in target sentences. |
| Approach: | They propose a frequency-based parser that computes the span-overlap score as the word sequence’s frequency in the PAS-equivalent sentence set and identifies the constituent structure by finding a constituent tree with the maximum span- overlap score. |
| Outcome: | The proposed method outperforms existing unsupervised parsers in eight out of ten languages and is more accurate than previous methods. |
An Imitation Learning Approach to Unsupervised Parsing (P19-1)
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| Challenge: | Unsupervised parsing is a form of reinforcement learning that improves syntactic structures but lacks interpretability due to its lack of ad hoc heuristics. |
| Approach: | They propose an unsupervised approach that transfers syntactic knowledge to a Tree-LSTM model with discrete parsing actions. |
| Outcome: | The proposed model outperforms existing models on the All Natural Language Inference dataset and achieves a new state of the art in terms of parsing F-score. |
Revisiting the Practical Effectiveness of Constituency Parse Extraction from Pre-trained Language Models (2022.coling-1)
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| Challenge: | Constituency Parse Extraction from Pre-trained Language Models (CPE-PLM) is a new paradigm that attempts to induce constituency parse trees based on the internal knowledge of pre-tried language models. |
| Approach: | They propose to use constituency parse trees from pre-trained language models to induce constituency trees by introducing a set of heterogeneous PLMs combined using two advanced ensemble methods. |
| Outcome: | The proposed approach is more effective than typical supervised parsers in few-shot settings. |