Papers by Keigo Takahashi
Unsupervised Attention-based Sentence-Level Meta-Embeddings from Contextualised Language Models (2022.lrec-1)
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| Challenge: | Existing methods for creating metaembeddings from static word embeddings have been proposed, but they are not tied to a particular downstream task. |
| Approach: | They propose a sentence-level meta-embedding learning method that takes contextualised word embedding models and learns a phrase embeddable that preserves complementary strengths of the input source NLMs. |
| Outcome: | The proposed method outperforms existing methods on semantic textual similarity benchmarks on a supervised baseline and on token-level embeddings. |
Suppressing Final Layer Hidden State Jumps in Transformer Pretraining (2026.findings-eacl)
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| Challenge: | Existing models exhibit only slight changes in the angular distance between the input and output hidden state vectors in the middle layers . |
| Approach: | They propose a jump-suppressing regularizer which penalizes large hidden state displacements near the final layer during pre-training. |
| Outcome: | The proposed method significantly reduces hidden state jumps in the final layer and increases model capacity. |