@inproceedings{wang-etal-2021-length,
title = "How Length Prediction Influence the Performance of Non-Autoregressive Translation?",
author = "Wang, Minghan and
Jiaxin, Guo and
Wang, Yuxia and
Chen, Yimeng and
Chang, Su and
Shang, Hengchao and
Zhang, Min and
Tao, Shimin and
Yang, Hao",
editor = "Bastings, Jasmijn and
Belinkov, Yonatan and
Dupoux, Emmanuel and
Giulianelli, Mario and
Hupkes, Dieuwke and
Pinter, Yuval and
Sajjad, Hassan",
booktitle = "Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.blackboxnlp-1.14",
doi = "10.18653/v1/2021.blackboxnlp-1.14",
pages = "205--213",
abstract = "Length prediction is a special task in a series of NAT models where target length has to be determined before generation. However, the performance of length prediction and its influence on translation quality has seldom been discussed. In this paper, we present comprehensive analyses on length prediction task of NAT, aiming to find the factors that influence performance, as well as how it associates with translation quality. We mainly perform experiments based on Conditional Masked Language Model (CMLM) (Ghazvininejad et al., 2019), a representative NAT model, and evaluate it on two language pairs, En-De and En-Ro. We draw two conclusions: 1) The performance of length prediction is mainly influenced by properties of language pairs such as alignment pattern, word order or intrinsic length ratio, and is also affected by the usage of knowledge distilled data. 2) There is a positive correlation between the performance of the length prediction and the BLEU score.",
}
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<abstract>Length prediction is a special task in a series of NAT models where target length has to be determined before generation. However, the performance of length prediction and its influence on translation quality has seldom been discussed. In this paper, we present comprehensive analyses on length prediction task of NAT, aiming to find the factors that influence performance, as well as how it associates with translation quality. We mainly perform experiments based on Conditional Masked Language Model (CMLM) (Ghazvininejad et al., 2019), a representative NAT model, and evaluate it on two language pairs, En-De and En-Ro. We draw two conclusions: 1) The performance of length prediction is mainly influenced by properties of language pairs such as alignment pattern, word order or intrinsic length ratio, and is also affected by the usage of knowledge distilled data. 2) There is a positive correlation between the performance of the length prediction and the BLEU score.</abstract>
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%0 Conference Proceedings
%T How Length Prediction Influence the Performance of Non-Autoregressive Translation?
%A Wang, Minghan
%A Jiaxin, Guo
%A Wang, Yuxia
%A Chen, Yimeng
%A Chang, Su
%A Shang, Hengchao
%A Zhang, Min
%A Tao, Shimin
%A Yang, Hao
%Y Bastings, Jasmijn
%Y Belinkov, Yonatan
%Y Dupoux, Emmanuel
%Y Giulianelli, Mario
%Y Hupkes, Dieuwke
%Y Pinter, Yuval
%Y Sajjad, Hassan
%S Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F wang-etal-2021-length
%X Length prediction is a special task in a series of NAT models where target length has to be determined before generation. However, the performance of length prediction and its influence on translation quality has seldom been discussed. In this paper, we present comprehensive analyses on length prediction task of NAT, aiming to find the factors that influence performance, as well as how it associates with translation quality. We mainly perform experiments based on Conditional Masked Language Model (CMLM) (Ghazvininejad et al., 2019), a representative NAT model, and evaluate it on two language pairs, En-De and En-Ro. We draw two conclusions: 1) The performance of length prediction is mainly influenced by properties of language pairs such as alignment pattern, word order or intrinsic length ratio, and is also affected by the usage of knowledge distilled data. 2) There is a positive correlation between the performance of the length prediction and the BLEU score.
%R 10.18653/v1/2021.blackboxnlp-1.14
%U https://aclanthology.org/2021.blackboxnlp-1.14
%U https://doi.org/10.18653/v1/2021.blackboxnlp-1.14
%P 205-213
Markdown (Informal)
[How Length Prediction Influence the Performance of Non-Autoregressive Translation?](https://aclanthology.org/2021.blackboxnlp-1.14) (Wang et al., BlackboxNLP 2021)
ACL
- Minghan Wang, Guo Jiaxin, Yuxia Wang, Yimeng Chen, Su Chang, Hengchao Shang, Min Zhang, Shimin Tao, and Hao Yang. 2021. How Length Prediction Influence the Performance of Non-Autoregressive Translation?. In Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pages 205–213, Punta Cana, Dominican Republic. Association for Computational Linguistics.