Papers by Taichi Aida
Can Word Sense Distribution Detect Semantic Changes of Words? (2023.findings-emnlp)
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| Challenge: | Existing methods to detect semantic variations of words are not accurate for time-sensitive predictions. |
| Approach: | They propose to use pretrained static sense embeddings to annotate a word's occurrence with a sense id to compare its distributions. |
| Outcome: | The proposed method compares word sense distributions across two corpora to predict meaning change . the results show that pretrained LLMs can detect changes in words over time . |
A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection (2024.findings-acl)
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| Challenge: | Existing Word-in-Context (WiC) datasets are used to detect temporal semantic changes of words. |
| Approach: | They propose a supervised two-staged SCD method that uses existing Word-in-Context (WiC) datasets to predict temporal semantic changes of words. |
| Outcome: | The proposed method achieves strong performance in multiple languages and significant improvements on WiC benchmarks. |
Construction of a Quality Estimation Dataset for Automatic Evaluation of Japanese Grammatical Error Correction (2022.lrec-1)
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Daisuke Suzuki, Yujin Takahashi, Ikumi Yamashita, Taichi Aida, Tosho Hirasawa, Michitaka Nakatsuji, Masato Mita, Mamoru Komachi
| Challenge: | Existing studies on automatic evaluation of grammatical error correction (GEC) have shown that quality estimation models built from manual evaluation can achieve high performance in automatic evaluation in English. |
| Approach: | They used a dataset with manual evaluation to build an automatic evaluation model for Japanese GEC. |
| Outcome: | The proposed model is based on a Japanese dataset with manual evaluation and meta-evaluation. |
Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddings (2023.findings-acl)
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| Challenge: | Existing work on semantic variation prediction has focused on comparing an averaged contextualised representation of a word . however, some of the previously associated meanings of . a target word can become obsolete over time, while novel usages of existing words are observed. |
| Approach: | They propose a method that uses the entire cohort of contextualised embeddings of a target word to detect the semantic variation of words. |
| Outcome: | The proposed method outperforms existing methods on a SemEval-2020 benchmark dataset and is comparable to the state-of-the-art. |
Analyzing Continuous Semantic Shifts with Diachronic Word Similarity Matrices (2025.coling-main)
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| Challenge: | Existing methods to analyze word sense proportions are insufficient for understanding semantic shifts . et al., 2018: semantic shift and its effects. |
| Approach: | They propose a framework for how semantic shifts occur over multiple time periods by using word embeddings. |
| Outcome: | The proposed framework can analyze semantic shifts over multiple time periods using word embeddings. |
SCDTour: Embedding Axis Ordering and Merging for Interpretable Semantic Change Detection (2025.findings-emnlp)
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| Challenge: | Existing methods to improve interpretability of SCD often lead to performance degradation . agglomerating axes produces a more refined set of word senses, which improves performance . |
| Approach: | They propose a method that orders and merges interpretable axes to improve SCD performance. |
| Outcome: | The proposed method preserves performance while maintaining high interpretability . it produces a more refined set of word senses, which improves performance . |
Investigating the Contextualised Word Embedding Dimensions Specified for Contextual and Temporal Semantic Changes (2025.coling-main)
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| Challenge: | Existing studies on the meaning of contextualised word embeddings (SCWEs) have not shown how meaning changes are encoded in the embeddable space. |
| Approach: | They compare pre-trained and fine-tuned contextualised word embeddings on contextual and temporal semantic change detection benchmarks. |
| Outcome: | The pre-trained and fine-tuned versions of (SCWE) and their fine- tuned versions on contextual and temporal semantic change detection benchmarks show that they represent semantic changes across all dimensions when fine--and that they are more efficient than ICA. |
Modeling Text using the Continuous Space Topic Model with Pre-Trained Word Embeddings (2021.acl-srw)
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| Challenge: | Existing topic models that extract latent topics from text are based on latent topic and do not use intermediate variables such as latent subjects. |
| Approach: | They propose a model that extends the continuous space topic model (CSTM) they pre-train word embeddings which capture the semantics of words and plug them into the CSTM . |
| Outcome: | The proposed model performs better than the baseline model in terms of perplexity and convergence speed. |
Swap and Predict – Predicting the Semantic Changes in Words across Corpora by Context Swapping (2023.findings-emnlp)
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| Challenge: | Detecting semantic changes of words is an important task for various NLP applications that must make time-sensitive predictions. |
| Approach: | They propose a method that randomly swaps contexts between two different corpora to detect whether a given word changes its meaning . they then use a pretrained masked language model to generate contextualised word embeddings of w, which are then used to predict the semantic changes of words in four languages . |
| Outcome: | The proposed method achieves significant performance improvements compared to baselines for the English semantic change prediction task. |