5 citations · 24 across the 14 of their papers we have counts for
14 papers
Is Japanese CCGBank empirically correct? A case study of passive and causative constructions
Daisuke Bekki, Hitomi Yanaka
The Japanese CCGBank serves as training and evaluation data for developing Japanese CCG parsers. However, since it is automatically generated from the Kyoto Corpus, a dependency tr…
Building a Video-and-Language Dataset with Human Actions for Multimodal Logical Inference
Riko Suzuki, Hitomi Yanaka, Koji Mineshima +1
This paper introduces a new video-and-language dataset with human actions for multimodal logical inference, which focuses on intentional and aspectual expressions that describe dyn…
Combining Event Semantics and Degree Semantics for Natural Language Inference
Izumi Haruta, Koji Mineshima, Daisuke Bekki
In formal semantics, there are two well-developed semantic frameworks: event semantics, which treats verbs and adverbial modifiers using the notion of event, and degree semantics,…
Logical Inferences with Comparatives and Generalized Quantifiers
Izumi Haruta, Koji Mineshima, Daisuke Bekki
Comparative constructions pose a challenge in Natural Language Inference (NLI), which is the task of determining whether a text entails a hypothesis. Comparatives are structurally…
Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language?
Hitomi Yanaka, Koji Mineshima, Daisuke Bekki +1
Despite the success of language models using neural networks, it remains unclear to what extent neural models have the generalization ability to perform inferences. In this paper,…
A CCG-based Compositional Semantics and Inference System for Comparatives
Izumi Haruta, Koji Mineshima, Daisuke Bekki
Comparative constructions play an important role in natural language inference. However, attempts to study semantic representations and logical inferences for comparatives from the…
Can neural networks understand monotonicity reasoning?
Hitomi Yanaka, Koji Mineshima, Daisuke Bekki +4
Monotonicity reasoning is one of the important reasoning skills for any intelligent natural language inference (NLI) model in that it requires the ability to capture the interactio…
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…
HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning
Hitomi Yanaka, Koji Mineshima, Daisuke Bekki +4
Large crowdsourced datasets are widely used for training and evaluating neural models on natural language inference (NLI). Despite these efforts, neural models have a hard time cap…
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…
Acquisition of Phrase Correspondences using Natural Deduction Proofs
Hitomi Yanaka, Koji Mineshima, Pascual Martinez-Gomez +1
How to identify, extract, and use phrasal knowledge is a crucial problem for the task of Recognizing Textual Entailment (RTE). To solve this problem, we propose a method for detect…
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…
Determining Semantic Textual Similarity using Natural Deduction Proofs
Hitomi Yanaka, Koji Mineshima, Pascual Martinez-Gomez +1
Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow informati…