Papers by Nobuyuki Iokawa

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Papers by Nobuyuki Iokawa

LREC

2024.lrec-main

deep learning-based approaches
logical inference
semantic representations of images
logic-based VTE system
model checking
computational cost
multimodal inference
visual-textual entailment
inference
VTE

2024

Multimodality

1 papers

Visual-Textual Entailment with Quantities Using Model Checking and Knowledge Injection (2024.lrec-main)

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Nobuyuki Iokawa, Hitomi Yanaka

Challenge:	Visual-textual entailment (VTE) is a critical task in multimodal inference.
Approach:	They propose a visual-textual entailment system that solves VTE tasks with quantities and negation.
Outcome:	The proposed system solves visual-textual entailment tasks with quantities and negation more robustly than previous approaches.

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