Papers with Combinatory Categorial Grammar
Compositional Semantics and Inference System for Temporal Order based on Japanese CCG (2022.acl-srw)
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| Challenge: | a system for temporal order in Japanese has not been developed for linguistic inference involving temporal expressions. |
| Approach: | They propose a Japanese NLI system that considers temporal order in Japanese . they use axioms for temporal relations and automated theorem provers to perform inference involving temporal orders. |
| Outcome: | The proposed system outperforms logic-based systems and current deep learning models on Japanese datasets. |
Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation (P19-1)
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| Challenge: | Existing methods for Combinatory Categorial Grammar (CCG) parsing are limited to a specific parser architecture, making it non-trivial to apply to current parsers. |
| Approach: | They propose a domain adaptation method for Combinatory Categorial Grammar (CCG) they propose to generate CCG corpora using cheaper dependency trees. |
| Outcome: | The proposed method improves on speech conversation and math problems. |
Reforging : A Method for Constructing a Linguistically Valid Japanese CCG Treebank (2024.eacl-srw)
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| Challenge: | Existing treebanks for Combinatory Categorial Grammar (CCG) are insufficient for linguistic validity of CCG . |
| Approach: | They propose to combine ABCTreebank and lightblue to generate a linguistically valid Japanese CCG treebank with detailed information by filtering lightblu's lexical items using ABCTtreebank. |
| Outcome: | The proposed method generates a linguistically valid Japanese CCG treebank with detailed information by combining the strengths of ABCTreebank and lightblue. |
Logical Inferences with Comparatives and Generalized Quantifiers (2020.acl-srw)
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| Challenge: | Comparative constructions pose a challenge in Natural Language Inference (NLI) Comparatives interact with quantifiers, numerals, and lexical antonyms, but a logical inference system for comparatives has not been developed for the task. |
| Approach: | They propose a compositional semantics system that maps comparative constructions to semantic representations via combinatory categorial grammar parsers and integrates it with an automated theorem proving system. |
| Outcome: | The proposed system outperforms previous logic-based systems and deep learning models on three NLI datasets. |
LLM-supertagger: Categorial Grammar Supertagging via Large Language Models (2024.findings-emnlp)
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| Challenge: | Recent studies have shown that LLMs are underperforming in classification tasks due to their decoder-based nature. |
| Approach: | They propose a method that significantly boosts LLMs' performance in supertagging for both Combinatory Categorial Grammar (CCG) and Lambek Categorian Grammar (LCG). |
| Outcome: | The proposed method outperforms LSTM and encoder-based models and achieves state-of-the-art performance. |
Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning (N18-2)
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| Challenge: | Existing methods to recognize textual entailment use a CCG parser to process sentences . failing to recognize the similar syntactic structure results in inconsistent argument structures . |
| Approach: | They propose to extend existing CCG parsers to parse sentences consistently . they use an inter-sentence modeling with Markov Random Fields to achieve this . |
| Outcome: | The proposed method improves on English and Japanese languages. |
Development of a Multilingual CCG Treebank via Universal Dependencies Conversion (2022.lrec-1)
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| Challenge: | Combinatory Categorial Grammar (CCG) is a lexicalized grammar formalism that can capture both syntactic and semantic information. |
| Approach: | They propose an algorithm to convert UD treebanks to CCG treebank and propose future extensions. |
| Outcome: | The proposed algorithm performs lexical, sentential, and syntactic rule coverage analysis, as well as CCG parsing experiments. |