1 citations · 2 across the 5 of their papers we have counts for
5 papers
End-to-End Trainable Retrieval-Augmented Generation for Relation Extraction
Kohei Makino, Makoto Miwa, Yutaka Sasaki
This paper addresses a crucial challenge in retrieval-augmented generation-based relation extractors; the end-to-end training is not applicable to conventional retrieval-augmented…
Span-based Named Entity Recognition by Generating and Compressing Information
Nhung T. H. Nguyen, Makoto Miwa, Sophia Ananiadou
The information bottleneck (IB) principle has been proven effective in various NLP applications. The existing work, however, only used either generative or information compression…
Analyzing Research Trends in Inorganic Materials Literature Using NLP
Fusataka Kuniyoshi, Jun Ozawa, Makoto Miwa
In the field of inorganic materials science, there is a growing demand to extract knowledge such as physical properties and synthesis processes of materials by machine-reading a la…
A Neural Edge-Editing Approach for Document-Level Relation Graph Extraction
Kohei Makino, Makoto Miwa, Yutaka Sasaki
In this paper, we propose a novel edge-editing approach to extract relation information from a document. We treat the relations in a document as a relation graph among entities in…
Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors
Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Kno…