NAIST English-to-Japanese Simultaneous Translation System for IWSLT 2021 Simultaneous Text-to-text Task
Ryo Fukuda | Yui Oka | Yasumasa Kano | Yuki Yano | Yuka Ko | Hirotaka Tokuyama | Kosuke Doi | Sakriani Sakti | Katsuhito Sudoh | Satoshi Nakamura
Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)
This paper describes NAIST’s system for the English-to-Japanese Simultaneous Text-to-text Translation Task in IWSLT 2021 Evaluation Campaign. Our primary submission is based on wait-k neural machine translation with sequence-level knowledge distillation to encourage literal translation.
Utterance Position-Aware Dialogue Act Recognition
Yuki Yano | Akihiro Tamura | Takashi Ninomiya | Hiroaki Obayashi
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
This study proposes an utterance position-aware approach for a neural network-based dialogue act recognition (DAR) model, which incorporates positional encoding for utterance’s absolute or relative position. The proposed approach is inspired by the observation that some dialogue acts have tendencies of occurrence positions. The evaluations on the Switchboard corpus show that the proposed positional encoding of utterances statistically significantly improves the performance of DAR.
- Ryo Fukuda 1
- Yui Oka 1
- Yasumasa Kano 1
- Yuka Ko 1
- Hirotaka Tokuyama 1
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