2022 EMNLP EMNLP 2022

Test Suite Evaluation: Morphological Challenges and Pronoun Translation

Abstract

AbstractThis paper summarizes the results of our test suite evaluation with a main focus on morphology for the language pairs English to/from German. We look at the translation of morphologically complex words (DE–EN), and evaluatewhether English noun phrases are translated as compounds vs. phrases into German. Furthermore, we investigate the preservation of morphological features (gender in EN–DE pronoun translation and number in morpho-syntacticallycomplex structures for DE–EN). Our results indicate that systems are able to interpret linguistic structures to obtain relevant information, but also that translation becomes more challenging with increasing complexity, as seen, for example, when translating words with negation or non-concatenative properties, and for the morecomplex cases of the pronoun translation task.

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🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio