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Multimodal Logical Inference System for Visual-Textual Entailment
Riko Suzuki, Hitomi Yanaka, Masashi Yoshikawa +2
A large amount of research about multimodal inference across text and vision has been recently developed to obtain visually grounded word and sentence representations. In this pape…
Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation
Masashi Yoshikawa, Hiroshi Noji, Koji Mineshima +1
We propose a new domain adaptation method for Combinatory Categorial Grammar (CCG) parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of…
Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference
Masashi Yoshikawa, Koji Mineshima, Hiroshi Noji +1
In logic-based approaches to reasoning tasks such as Recognizing Textual Entailment (RTE), it is important for a system to have a large amount of knowledge data. However, there is…
Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning
Masashi Yoshikawa, Koji Mineshima, Hiroshi Noji +1
In formal logic-based approaches to Recognizing Textual Entailment (RTE), a Combinatory Categorial Grammar (CCG) parser is used to parse input premises and hypotheses to obtain the…
A* CCG Parsing with a Supertag and Dependency Factored Model
Masashi Yoshikawa, Hiroshi Noji, Yuji Matsumoto
We propose a new A* CCG parsing model in which the probability of a tree is decomposed into factors of CCG categories and its syntactic dependencies both defined on bi-directional…