43 citations · 54 across the 29 of their papers we have counts for
30 papers
FrameBench:A Language Understanding Benchmark Based on Frame Semantics
Chihiro Yano, Ryohei Sasano
In frame semantics, sentence comprehension is assumed to proceed by relating lexical meaning to background knowledge called semantic frames, thereby enabling readers to implicitly…
When Is 0.1% Enough? Analyzing the Combined Effects of Dimensionality Reduction and Quantization on Text Embedding Compression
Riku Kisako, Hayato Tsukagoshi, Ryohei Sasano
Recent high-performing text embedding models often produce high-dimensional real-valued vectors, resulting in substantial storage and computational costs. To address this issue, em…
Can We Still Trace L1 Signals? Investigating the Resilience of Native Language Signals in the LLM Era
Nabelanita Utami, Ryohei Sasano
The widespread use of LLM-based writing assistance has raised an interesting question about the homogenization of English. As LLMs tend to revise texts toward mainstream English co…
How Do Language Models Acquire Character-Level Information?
Soma Sato, Ryohei Sasano
Language models (LMs) have been reported to implicitly encode character-level information, despite not being explicitly provided during training. However, the mechanisms underlying…
Do LLMs and Humans Find the Same Questions Difficult? A Case Study on Japanese Quiz Answering
Naoya Sugiura, Kosuke Yamada, Yasuhiro Ogawa +2
LLMs have achieved performance that surpasses humans in many NLP tasks. However, it remains unclear whether problems that are difficult for humans are also difficult for LLMs. This…
FrameEOL: Semantic Frame Induction using Causal Language Models
Chihiro Yano, Kosuke Yamada, Hayato Tsukagoshi +2
Semantic frame induction is the task of clustering frame-evoking words according to the semantic frames they evoke. In recent years, leveraging embeddings of frame-evoking words th…
Redundancy, Isotropy, and Intrinsic Dimensionality of Prompt-based Text Embeddings
Hayato Tsukagoshi, Ryohei Sasano
Prompt-based text embedding models, which generate task-specific embeddings upon receiving tailored prompts, have recently demonstrated remarkable performance. However, their resul…
On Representational Dissociation of Language and Arithmetic in Large Language Models
Riku Kisako, Tatsuki Kuribayashi, Ryohei Sasano
The association between language and (non-linguistic) thinking ability in humans has long been debated, and recently, neuroscientific evidence of brain activity patterns has been c…
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…
Ruri: Japanese General Text Embeddings
Hayato Tsukagoshi, Ryohei Sasano
We report the development of Ruri, a series of Japanese general text embedding models. While the development of general-purpose text embedding models in English and multilingual co…
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…
To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in Japanese
Yukiko Ishizuki, Tatsuki Kuribayashi, Yuichiroh Matsubayashi +2
Speakers sometimes omit certain arguments of a predicate in a sentence; such omission is especially frequent in pro-drop languages. This study addresses a question about ellipsis -…
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…
Japanese SimCSE Technical Report
Hayato Tsukagoshi, Ryohei Sasano, Koichi Takeda
We report the development of Japanese SimCSE, Japanese sentence embedding models fine-tuned with SimCSE. Since there is a lack of sentence embedding models for Japanese that can be…
Transformer-based Live Update Generation for Soccer Matches from Microblog Posts
Masashi Oshika, Kosuke Yamada, Ryohei Sasano +1
It has been known to be difficult to generate adequate sports updates from a sequence of vast amounts of diverse live tweets, although the live sports viewing experience with tweet…
Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction
Kosuke Yamada, Ryohei Sasano, Koichi Takeda
The semantic frame induction tasks are defined as a clustering of words into the frames that they evoke, and a clustering of their arguments according to the frame element roles th…
Sentence Representations via Gaussian Embedding
Shohei Yoda, Hayato Tsukagoshi, Ryohei Sasano +1
Recent progress in sentence embedding, which represents the meaning of a sentence as a point in a vector space, has achieved high performance on tasks such as a semantic textual si…
Semantic Frame Induction with Deep Metric Learning
Kosuke Yamada, Ryohei Sasano, Koichi Takeda
Recent studies have demonstrated the usefulness of contextualized word embeddings in unsupervised semantic frame induction. However, they have also revealed that generic contextual…
Cross-Modal Similarity-Based Curriculum Learning for Image Captioning
Hongkuan Zhang, Saku Sugawara, Akiko Aizawa +3
Image captioning models require the high-level generalization ability to describe the contents of various images in words. Most existing approaches treat the image-caption pairs eq…
Comparison and Combination of Sentence Embeddings Derived from Different Supervision Signals
Hayato Tsukagoshi, Ryohei Sasano, Koichi Takeda
There have been many successful applications of sentence embedding methods. However, it has not been well understood what properties are captured in the resulting sentence embeddin…
Transformer-based Lexically Constrained Headline Generation
Kosuke Yamada, Yuta Hitomi, Hideaki Tamori +4
This paper explores a variant of automatic headline generation methods, where a generated headline is required to include a given phrase such as a company or a product name. Previo…