Papers with PAS
Distance-Free Modeling of Multi-Predicate Interactions in End-to-End Japanese Predicate-Argument Structure Analysis (C18-1)
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| Challenge: | Existing models for analyzing PASs in Japanese are lacking in identifying elliptical arguments. |
| Approach: | They propose to extend the input and last layers of a bidirectional recurrent neural network model to capture the potential interactions among multiple PASs. |
| Outcome: | The proposed models improve prediction accuracy on a benchmark corpus and achieve state-of-the-art on standardized corpus. |
Machine Comprehension Improves Domain-Specific Japanese Predicate-Argument Structure Analysis (D19-58)
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| Challenge: | a lack of gold datasets and knowledge about PAS analysis makes it difficult to create accurate PAS analyses. |
| Approach: | They construct a Japanese blog-QA dataset and a reading comprehension QA dataset using crowdsourcing. |
| Outcome: | The proposed method is most effective, pre-training model to acquire domain knowledge and fine-tuning model based on PAS-QA dataset. |
Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis (P18-1)
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| Challenge: | Japanese predicate-argument structure analysis involves zero anaphora resolution . state-of-the-art models for PAS analysis achieve an accuracy of around 50% for zero pronouns . |
| Approach: | They propose a Japanese PAS analysis model based on semi-supervised adversarial training with a raw corpus. |
| Outcome: | The proposed model outperforms existing models for Japanese PAS analysis . the model is based on semi-supervised adversarial training with a raw corpus . |
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. |
Compositional Semantic Parsing across Graphbanks (P19-1)
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| Challenge: | Existing semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. |
| Approach: | They propose a compositional neural semantic parser which achieves competitive accuracies across graphbanks. |
| Outcome: | The proposed system achieves competitive accuracies across a variety of graphbanks. |
Few-Shot Semantic Dependency Parsing via Graph Contrastive Learning (2024.lrec-main)
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| Challenge: | Existing graph neural networks (GNNs) have shown promising performance on semantic dependency parsing (SDP) training a high-performing model requires a large amount of labeled data and it is prone to over-fitting in the absence of sufficient labele . |
| Approach: | They propose a syntax-guided graph contrastive learning framework to train GNNs with unlabeled data and fine-tune pre-trained GNN models with few-shot labeled SDP data. |
| Outcome: | The proposed framework achieves promising results when few-shot training samples are available. |