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    <abstract>Evaluation of cross-lingual encoders is usually performed either via zero-shot cross-lingual transfer in supervised downstream tasks or via unsupervised cross-lingual textual similarity. In this paper, we concern ourselves with reference-free machine translation (MT) evaluation where we directly compare source texts to (sometimes low-quality) system translations, which represents a natural adversarial setup for multilingual encoders. Reference-free evaluation holds the promise of web-scale comparison of MT systems. We systematically investigate a range of metrics based on state-of-the-art cross-lingual semantic representations obtained with pretrained M-BERT and LASER. We find that they perform poorly as semantic encoders for reference-free MT evaluation and identify their two key limitations, namely, (a) a semantic mismatch between representations of mutual translations and, more prominently, (b) the inability to punish “translationese”, i.e., low-quality literal translations. We propose two partial remedies: (1) post-hoc re-alignment of the vector spaces and (2) coupling of semantic-similarity based metrics with target-side language modeling. In segment-level MT evaluation, our best metric surpasses reference-based BLEU by 5.7 correlation points.</abstract>
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%0 Conference Proceedings
%T On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation
%A Zhao, Wei
%A Glavaš, Goran
%A Peyrard, Maxime
%A Gao, Yang
%A West, Robert
%A Eger, Steffen
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zhao-etal-2020-limitations
%X Evaluation of cross-lingual encoders is usually performed either via zero-shot cross-lingual transfer in supervised downstream tasks or via unsupervised cross-lingual textual similarity. In this paper, we concern ourselves with reference-free machine translation (MT) evaluation where we directly compare source texts to (sometimes low-quality) system translations, which represents a natural adversarial setup for multilingual encoders. Reference-free evaluation holds the promise of web-scale comparison of MT systems. We systematically investigate a range of metrics based on state-of-the-art cross-lingual semantic representations obtained with pretrained M-BERT and LASER. We find that they perform poorly as semantic encoders for reference-free MT evaluation and identify their two key limitations, namely, (a) a semantic mismatch between representations of mutual translations and, more prominently, (b) the inability to punish “translationese”, i.e., low-quality literal translations. We propose two partial remedies: (1) post-hoc re-alignment of the vector spaces and (2) coupling of semantic-similarity based metrics with target-side language modeling. In segment-level MT evaluation, our best metric surpasses reference-based BLEU by 5.7 correlation points.
%R 10.18653/v1/2020.acl-main.151
%U https://aclanthology.org/2020.acl-main.151/
%U https://doi.org/10.18653/v1/2020.acl-main.151
%P 1656-1671

Markdown (Informal)

[On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation](https://aclanthology.org/2020.acl-main.151/) (Zhao et al., ACL 2020)

• On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation (Zhao et al., ACL 2020)

ACL

• Wei Zhao, Goran Glavaš, Maxime Peyrard, Yang Gao, Robert West, and Steffen Eger. 2020. On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1656–1671, Online. Association for Computational Linguistics.