5 citations · 25 across the 13 of their papers we have counts for
14 papers · 1 filter
SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics
Hitomi Yanaka, Koji Mineshima, Kentaro Inui
Recently, deep neural networks (DNNs) have achieved great success in semantically challenging NLP tasks, yet it remains unclear whether DNN models can capture compositional meaning…
Exploring Transitivity in Neural NLI Models through Veridicality
Hitomi Yanaka, Koji Mineshima, Kentaro Inui
Despite the recent success of deep neural networks in natural language processing, the extent to which they can demonstrate human-like generalization capacities for natural languag…
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…