Papers by Tomoharu Iwata
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. |
Context-aware Neural Machine Translation with Mini-batch Embedding (2021.eacl-main)
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| Challenge: | Existing models that translate sentences in sentence-by-sentence manner do not consider the inter-sentent context in a language translation model. |
| Approach: | They propose to use mini-batch embedding to represent the features of sentences in a mini-Batch by choosing sentences from the same document and then embedd it into the model. |
| Outcome: | The proposed method outperforms baseline models and improves writing style or terminology to fit the document’s context. |