Papers by Xiangheng He
Language Model Based Unsupervised Dependency Parsing with Conditional Mutual Information and Grammatical Constraints (2024.naacl-long)
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| Challenge: | Existing methods for unsupervised dependency parsing use difficult to interpret dependence scores. |
| Approach: | They propose to use Conditional Mutual Information (CMI) to measure bi-lexical dependence and incorporate grammatical constraints into unsupervised parsing. |
| Outcome: | The proposed model outperforms state-of-the-art models and grammar-based models in five languages and eight datasets. |
Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models (2022.acl-long)
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| Challenge: | Existing methods for SRL identify semantic dependencies that specify the semantic role of arguments in relation to predicates. |
| Approach: | They propose a mixture model-based end-to-end method to model syntactic-semantic dependency correlation in Semantic Role Labeling. |
| Outcome: | The proposed method improves performance in English, German, and Spanish . it achieves small but statistically significant improvement over baseline methods . |
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. |