Viterbi Decoding of Directed Acyclic Transformer for Non-Autoregressive Machine Translation

• URL: http://arxiv.org/abs/2210.05193v1
• Date: Tue, 11 Oct 2022 06:53:34 GMT
• Title: Viterbi Decoding of Directed Acyclic Transformer for Non-Autoregressive Machine Translation
• Authors: Chenze Shao and Zhengrui Ma and Yang Feng
• Abstract summary: Non-autoregressive models achieve significant decoding speedup in neural machine translation but lack the ability to capture sequential dependency. We present a Viterbi decoding framework for DA-Transformer, which guarantees to find the joint optimal solution for the translation and decoding path under any length constraint.
• Score: 13.474844448367367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Non-autoregressive models achieve significant decoding speedup in neural machine translation but lack the ability to capture sequential dependency. Directed Acyclic Transformer (DA-Transformer) was recently proposed to model sequential dependency with a directed acyclic graph. Consequently, it has to apply a sequential decision process at inference time, which harms the global translation accuracy. In this paper, we present a Viterbi decoding framework for DA-Transformer, which guarantees to find the joint optimal solution for the translation and decoding path under any length constraint. Experimental results demonstrate that our approach consistently improves the performance of DA-Transformer while maintaining a similar decoding speedup.

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