1 citations · 2 across the 9 of their papers we have counts for
6 papers · 1 filter
CiMaTe: Citation Count Prediction Effectively Leveraging the Main Text
Jun Hirako, Ryohei Sasano, Koichi Takeda
Prediction of the future citation counts of papers is increasingly important to find interesting papers among an ever-growing number of papers. Although a paper's main text is an i…
Are Social Sentiments Inherent in LLMs? An Empirical Study on Extraction of Inter-demographic Sentiments
Kunitomo Tanaka, Ryohei Sasano, Koichi Takeda
Large language models (LLMs) are supposed to acquire unconscious human knowledge and feelings, such as social common sense and biases, by training models from large amounts of text…
Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLMs
Masashi Oshika, Makoto Morishita, Tsutomu Hirao +2
In recent years, neural machine translation (NMT) has been widely used in everyday life. However, the current NMT lacks a mechanism to adjust the difficulty level of translations t…
WikiSplit++: Easy Data Refinement for Split and Rephrase
Hayato Tsukagoshi, Tsutomu Hirao, Makoto Morishita +3
The task of Split and Rephrase, which splits a complex sentence into multiple simple sentences with the same meaning, improves readability and enhances the performance of downstrea…
Verifying Claims About Metaphors with Large-Scale Automatic Metaphor Identification
Kotaro Aono, Ryohei Sasano, Koichi Takeda
There are several linguistic claims about situations where words are more likely to be used as metaphors. However, few studies have sought to verify such claims with large corpora.…
Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot Examples
Soma Sato, Hayato Tsukagoshi, Ryohei Sasano +1
Decoder-based large language models (LLMs) have shown high performance on many tasks in natural language processing. This is also true for sentence embedding learning, where a deco…