Papers by Hayato Tsukagoshi
FrameEOL: Semantic Frame Induction using Causal Language Models (2025.findings-emnlp)
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| Challenge: | Semantic frame induction is the task of clustering frame-evoking words according to the semantic frames they evoke. |
| Approach: | They propose a prompt-based method for obtaining Frame Embeddings that outputs One frame-name as a Label . |
| Outcome: | The proposed method outperforms existing methods on English and Japanese datasets. |
Comparison and Combination of Sentence Embeddings Derived from Different Supervision Signals (2022.starsem-1)
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| Challenge: | Existing methods to derive sentence embeddings have not been well understood what properties are captured in the resulting sentences depending on the supervision signals. |
| Approach: | They propose to combine two types of sentence embedding methods with similar architectures and tasks to investigate their properties. |
| Outcome: | The proposed methods perform better on unsupervised and downstream tasks than the proposed methods on untrained STS tasks and probing tasks. |
WikiSplit++: Easy Data Refinement for Split and Rephrase (2024.lrec-main)
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| Challenge: | Existing text simplification methods rely on encoder-decoder models to achieve this task. |
| Approach: | They propose a text-to-text generation approach that applies encoder-decoder models to a large-scale dataset to improve Split and Rephrase. |
| Outcome: | The proposed approach improves Split and Rephrase readability and performance on large datasets, but still suffers from hallucinations and under-splitting. |
Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot Examples (2024.acl-srw)
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| Challenge: | Decoder-based large language models (LLMs) have shown high performance on many tasks in natural language processing. |
| Approach: | They propose to automatically generate an NLI dataset with an LLM and use it for fine-tuning of PromptEOL. |
| Outcome: | The proposed model outperforms existing models on STS tasks without large manually annotated datasets. |
Redundancy, Isotropy, and Intrinsic Dimensionality of Prompt-based Text Embeddings (2025.findings-acl)
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| Challenge: | Prompt-based text embedding models generate task-specific embeddables but have thousands of dimensions . dimensionality reductions for embedded text can result in performance degradations of only the first 25% of the dimensions resulting in a very small degradation . |
| Approach: | They investigate how post-hoc dimensionality reduction affects performance of various tasks . they find that embeddings for classification and clustering exhibit lower intrinsic dimensionalities . |
| Outcome: | The proposed model generates task-specific embeddings upon receiving tailored prompts, but has thousands of dimensions and high storage costs. |
DefSent: Sentence Embeddings using Definition Sentences (2021.acl-short)
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| Challenge: | Sentence embedding methods using natural language inference datasets are limited for limited languages due to large datasets. |
| Approach: | They propose a sentence embedding method that uses definition sentences from a word dictionary. |
| Outcome: | The proposed method performs comparably on unsupervised semantics textual similarity tasks and slightly better on SentEval tasks than methods using large NLI datasets. |
Sentence Representations via Gaussian Embedding (2024.eacl-short)
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| Challenge: | Sentence embeddings represent a sentence's meaning as a point in a vector space and primarily use symmetric measures such as the cosine similarity to measure the similarity between sentences, they cannot capture asymmetric relationships between two sentences, such as entailment and hierarchical relations. |
| Approach: | They propose a Gaussian-distribution-based contrastive learning framework for sentence embedding that can handle asymmetric inter-sentential relations and a similarity measure for identifying entailment relations. |
| Outcome: | The proposed framework performs comparable to that of previous methods on natural language inference tasks and estimates direction of entailment relations, which is difficult with point representations. |