2 citations · 2 across the 10 of their papers we have counts for
10 papers
Training-free Conditional Image Embedding Framework Leveraging Large Vision Language Models
Masayuki Kawarada, Kosuke Yamada, Antonio Tejero-de-Pablos +1
Conditional image embeddings are feature representations that focus on specific aspects of an image indicated by a given textual condition (e.g., color, genre), which has been a ch…
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…
Out-of-the-Box Conditional Text Embeddings from Large Language Models
Kosuke Yamada, Peinan Zhang
Conditional text embedding is a proposed representation that captures the shift in perspective on texts when conditioned on a specific aspect. Previous methods have relied on exten…
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…
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…
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…
Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering
Kosuke Yamada, Ryohei Sasano, Koichi Takeda
Recent studies on semantic frame induction show that relatively high performance has been achieved by using clustering-based methods with contextualized word embeddings. However, t…
Verb Sense Clustering using Contextualized Word Representations for Semantic Frame Induction
Kosuke Yamada, Ryohei Sasano, Koichi Takeda
Contextualized word representations have proven useful for various natural language processing tasks. However, it remains unclear to what extent these representations can cover han…