We show that the state-of-the-art Transformer MT model is not biased towards monotonic reordering (unlike previous recurrent neural network models), but that nevertheless, long-distance dependencies remain a challenge for the model. Since most dependencies are short-distance, common evaluation metrics will be little influenced by how well systems perform on them. We therefore propose an automatic approach for extracting challenge sets rich with long-distance dependencies, and argue that evaluation using this methodology provides a complementary perspective on system performance. To support our claim, we compile challenge sets for English-German and German-English, which are much larger than any previously released challenge set for MT. The extracted sets are large enough to allow reliable automatic evaluation, which makes the proposed approach a scalable and practical solution for evaluating MT performance on the long-tail of syntactic phenomena.
@inproceedings{DBLP:conf/conll/ChoshenA19,
author = {Leshem Choshen and
Omri Abend},
editor = {Mohit Bansal and
Aline Villavicencio},
title = {Automatically Extracting Challenge Sets for Non-Local Phenomena in
Neural Machine Translation},
booktitle = {Proceedings of the 23rd Conference on Computational Natural Language
Learning, CoNLL 2019, Hong Kong, China, November 3-4, 2019},
pages = {291--303},
publisher = {Association for Computational Linguistics},
year = {2019},
url = {https://doi.org/10.18653/v1/K19-1028},
doi = {10.18653/V1/K19-1028},
timestamp = {Thu, 05 Aug 2021 17:36:17 +0200},
biburl = {https://dblp.org/rec/conf/conll/ChoshenA19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
See the full reference list in the paper.