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Dynamic Self-Attention : Computing Attention over Words Dynamically for Sentence Embedding

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arxiv 1808.07383 v1 pith:DSU2SA2A submitted 2018-08-22 cs.LG cs.CLstat.ML

Deunsol Yoon , Dongbok Lee , SangKeun Lee This is my paper · ORCID
classification cs.LGcs.CLstat.ML
keywords dynamicself-attentionsentencedatasetembeddinglanguagenaturalresults
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In this paper, we propose Dynamic Self-Attention (DSA), a new self-attention mechanism for sentence embedding. We design DSA by modifying dynamic routing in capsule network (Sabouretal.,2017) for natural language processing. DSA attends to informative words with a dynamic weight vector. We achieve new state-of-the-art results among sentence encoding methods in Stanford Natural Language Inference (SNLI) dataset with the least number of parameters, while showing comparative results in Stanford Sentiment Treebank (SST) dataset.

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    Combining schema, chain-of-thought, metadata knowledge, and tokenized prompt structures during fine-tuning improves text-to-SQL execution accuracy on private business databases compared to schema-only fine-tuning.

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