5 citations · 21 across the 10 of their papers we have counts for
12 papers · 1 filter
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…