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Distance-based Directional Self-Attention Network : 거리 및 방향 기반 Self-Attention Network
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- Authors
- Advisor
- 조성준
- Major
- 공과대학 산업공학과
- Issue Date
- 2018-02
- Publisher
- 서울대학교 대학원
- Keywords
- Attention mechanism ; Distance between words ; Distance mask ; Local dependency ; Global dependency ; Natural Language Inference
- Description
- 학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 2. 조성준.
- Abstract
- Attention mechanism has been used as an ancillary means to help RNN or CNN. However, the Transformer (Vaswani et al., 2017) recently recorded the state-of-the-art performance in machine translation with a dramatic reduction in training time by solely using attention. Motivated by the Transformer, Directional Self Attention Network (Shen et al., 2017), a fully attention-based sentence encoder, was proposed. It showed good performance with various data by using forward and backward directional information in a sentence. But in their study, not considered at all was the distance between words, an important feature when learning the local dependency to help understand the context of input text. We propose Distance-based Directional Self-Attention Network, which considers the word distance by using a simple distance mask in order to model the local dependency without losing the ability of modeling global dependency which attention has inherent. Our model shows good performance with NLI data, and it records the new state-of-the-art result with SNLI data. Additionally, we show that our model has a strength in long sentences or documents.
- Language
- English
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