Zobrazeno 1 - 10
of 278
pro vyhledávání: '"MIWA, MAKOTO"'
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 generation due to the non-differentiable nature of instance retrieval.
Externí odkaz:
http://arxiv.org/abs/2406.03790
The information bottleneck (IB) principle has been proven effective in various NLP applications. The existing work, however, only used either generative or information compression models to improve the performance of the target task. In this paper, w
Externí odkaz:
http://arxiv.org/abs/2302.05392
Negation and uncertainty modeling are long-standing tasks in natural language processing. Linguistic theory postulates that expressions of negation and uncertainty are semantically independent from each other and the content they modify. However, pre
Externí odkaz:
http://arxiv.org/abs/2204.00511
Autor:
Futase, Hayato, Tsujimura, Tomoki, Kajimoto, Tetsuya, Kawarazaki, Hajime, Suzuki, Toshiyuki, Miwa, Makoto, Sasaki, Yutaka
Generative Adversarial Networks (GANs) have shown remarkable successes in generating realistic images and interpolating changes between images. Existing models, however, do not take into account physical contexts behind images in generating the image
Externí odkaz:
http://arxiv.org/abs/2110.04077
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 large number of papers. This is because materials researchers refer to m
Externí odkaz:
http://arxiv.org/abs/2106.14157
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 this approach. The relation graph is iteratively constructed by editin
Externí odkaz:
http://arxiv.org/abs/2106.09900
We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Knowledge Base pairs. To achieve this, we bias the latent space of senten
Externí odkaz:
http://arxiv.org/abs/2104.08225
Publikováno v:
In Computer Speech & Language March 2024 84
The synthesis process is essential for achieving computational experiment design in the field of inorganic materials chemistry. In this work, we present a novel corpus of the synthesis process for all-solid-state batteries and an automated machine re
Externí odkaz:
http://arxiv.org/abs/2002.07339
We tackle the nested and overlapping event detection task and propose a novel search-based neural network (SBNN) structured prediction model that treats the task as a search problem on a relation graph of trigger-argument structures. Unlike existing
Externí odkaz:
http://arxiv.org/abs/1910.10281