We present the task of PreQuEL, Pre-(Quality-Estimation) Learning. A PreQuEL system predicts how well a given sentence will be translated, without recourse to the actual translation, thus eschewing unnecessary resource allocation when translation quality is bound to be low. PreQuEL can be defined relative to a given MT system (e.g., some industry service) or generally relative to the state-of-the-art. From a theoretical perspective, PreQuEL places the focus on the source text, tracing properties, possibly linguistic features, that make a sentence harder to machine translate. We develop a baseline model for the task and analyze its performance. We also develop a data augmentation method (from parallel corpora), that improves results substantially. We show that this augmentation method can improve the performance of the Quality-Estimation task as well. We investigate the properties of the input text that our model is sensitive to, by testing it on challenge sets and different languages. We conclude that it is aware of syntactic and semantic distinctions, and correlates and even over-emphasizes the importance of standard NLP features.
|Original language||American English|
|Number of pages||14|
|State||Published - 2022|
|Event||2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 - Abu Dhabi, United Arab Emirates|
Duration: 7 Dec 2022 → 11 Dec 2022
|Conference||2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022|
|Country/Territory||United Arab Emirates|
|Period||7/12/22 → 11/12/22|
Bibliographical noteFunding Information:
We thank Anna Pellivert and Menachem Shefer for helpful discussions. This work was supported in part by the Israel Science Foundation (grant no. 2424/21), and by the Applied Research in Academia Program of the Israel Innovation Authority.
© 2022 Association for Computational Linguistics.