Papers by Takashi Wada
Unsupervised Multilingual Word Embedding with Limited Resources using Neural Language Models (P19-1)
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| Challenge: | Existing methods that map word embeddings into a common space without any parallel data or pre-training have been proposed that are limited in resources and perform poorly under resource-poor conditions. |
| Approach: | They propose a model that maps monolingual word embeddings into a common space without any parallel data and generates multilingual embeddables without any pre-training. |
| Outcome: | The proposed model outperforms existing methods on word alignment tasks on low-resource conditions and with limited resources. |
Unsupervised Lexical Simplification with Context Augmentation (2023.findings-emnlp)
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| Challenge: | Existing unsupervised lexical simplification methods only use monolingual data and pre-trained models. |
| Approach: | They propose an unsupervised method that generates substitutes based on monolingual data and pre-trained language models. |
| Outcome: | The proposed method outperforms existing models on the TSAR-2022 task in English, Portuguese, and Spanish. |
Unsupervised Paraphrasing of Multiword Expressions (2023.findings-acl)
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| Challenge: | Existing methods for paraphrasing multiword expressions in context are unsupervised . multiwords are notoriously difficult to model because the meaning of the whole can diverge substantially from that of the component words. |
| Approach: | They propose an unsupervised approach to paraphrasing multiword expressions in context using monolingual corpus data and pre-trained language models. |
| Outcome: | The proposed method outperforms all unsupervised systems and rivals supervised systems on the SemEval 2022 idiomatic text similarity task. |
Static Word Embeddings for Sentence Semantic Representation (2025.emnlp-main)
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| Challenge: | Existing methods to learn fixed-length embeddings for sentence semantics require large computational cost, making it difficult to process billions of sentences cost-efficiently or deploy models on resource-constrained devices such as smartphones. |
| Approach: | They propose to extract word embeddings from a pre-trained Sentence Transformer and improve them with sentence-level principal component analysis followed by knowledge distillation or contrastive learning. |
| Outcome: | The proposed model outperforms existing models on sentence semantic tasks and surpasses a basic Sentence Transformer model (SimCSE) on a text embedding benchmark. |
Unsupervised Lexical Substitution with Decontextualised Embeddings (2022.coling-1)
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| Challenge: | Existing methods for lexical substitution using pre-trained language models have some limitations. |
| Approach: | They propose an unsupervised method for lexical substitution using pre-trained language models. |
| Outcome: | The proposed method outperforms baseline models and establishes a state-of-the-art without supervision or fine-tuning. |
Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation (2020.coling-main)
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Yuya Sawada, Takashi Wada, Takayoshi Shibahara, Hiroki Teranishi, Shuhei Kondo, Hiroyuki Shindo, Taro Watanabe, Yuji Matsumoto
| Challenge: | a new method for nominal coordination boundary identification is proposed . it uses pre-trained word embeddings to measure similarities of words and detects the span of coordination . |
| Approach: | They propose a method for nominal coordination boundary identification that uses pre-trained word embeddings to measure similarities of words and detects the span of coordination. |
| Outcome: | The proposed method can identify coordination boundaries without training on labeled data . it is comparable to a recent supervised method for the case when the coordinator conjoins simple noun phrases. |