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    <abstract>This paper proposes a novel architecture, Cross Attention Augmented Transducer (CAAT), for simultaneous translation. The framework aims to jointly optimize the policy and translation models. To effectively consider all possible READ-WRITE simultaneous translation action paths, we adapt the online automatic speech recognition (ASR) model, RNN-T, but remove the strong monotonic constraint, which is critical for the translation task to consider reordering. To make CAAT work, we introduce a novel latency loss whose expectation can be optimized by a forward-backward algorithm. We implement CAAT with Transformer while the general CAAT architecture can also be implemented with other attention-based encoder-decoder frameworks. Experiments on both speech-to-text (S2T) and text-to-text (T2T) simultaneous translation tasks show that CAAT achieves significantly better latency-quality trade-offs compared to the state-of-the-art simultaneous translation approaches.</abstract>
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%0 Conference Proceedings
%T Cross Attention Augmented Transducer Networks for Simultaneous Translation
%A Liu, Dan
%A Du, Mengge
%A Li, Xiaoxi
%A Li, Ya
%A Chen, Enhong
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F liu-etal-2021-cross
%X This paper proposes a novel architecture, Cross Attention Augmented Transducer (CAAT), for simultaneous translation. The framework aims to jointly optimize the policy and translation models. To effectively consider all possible READ-WRITE simultaneous translation action paths, we adapt the online automatic speech recognition (ASR) model, RNN-T, but remove the strong monotonic constraint, which is critical for the translation task to consider reordering. To make CAAT work, we introduce a novel latency loss whose expectation can be optimized by a forward-backward algorithm. We implement CAAT with Transformer while the general CAAT architecture can also be implemented with other attention-based encoder-decoder frameworks. Experiments on both speech-to-text (S2T) and text-to-text (T2T) simultaneous translation tasks show that CAAT achieves significantly better latency-quality trade-offs compared to the state-of-the-art simultaneous translation approaches.
%R 10.18653/v1/2021.emnlp-main.4
%U https://aclanthology.org/2021.emnlp-main.4
%U https://doi.org/10.18653/v1/2021.emnlp-main.4
%P 39-55

Markdown (Informal)

[Cross Attention Augmented Transducer Networks for Simultaneous Translation](https://aclanthology.org/2021.emnlp-main.4) (Liu et al., EMNLP 2021)

• Cross Attention Augmented Transducer Networks for Simultaneous Translation (Liu et al., EMNLP 2021)

ACL

• Dan Liu, Mengge Du, Xiaoxi Li, Ya Li, and Enhong Chen. 2021. Cross Attention Augmented Transducer Networks for Simultaneous Translation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 39–55, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.