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arXiv · 2405.02673

On the Information Redundancy in Non-Autoregressive Translation

Zhihao Wang·Longyue Wang·Jinsong Su·Junfeng Yao·Zhaopeng Tu
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Abstract

Token repetition is a typical form of multi-modal problem in fully non-autoregressive translation (NAT). In this work, we revisit the multi-modal problem in recently proposed NAT models. Our study reveals that these advanced models have introduced other types of information redundancy errors, which cannot be measured by the conventional metric - the continuous repetition ratio. By manually annotating the NAT outputs, we identify two types of information redundancy errors that correspond well to lexical and reordering multi-modality problems. Since human annotation is time-consuming and labor-intensive, we propose automatic metrics to evaluate the two types of redundant errors. Our metrics allow future studies to evaluate new methods and gain a more comprehensive understanding of their effectiveness.

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Zhihao Wang, Longyue Wang, Jinsong Su, Junfeng Yao, Zhaopeng Tu. 2024-05-04. On the Information Redundancy in Non-Autoregressive Translation. https://arxiv.org/abs/2405.02673

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