Part of Speech and Universal Dependency effects on English Arabic Machine Translation
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Abstract (arXiv)
In this research paper, I will elaborate on a method to evaluate machine translation models based on their performance on underlying syntactical phenomena between English and Arabic languages. This method is especially important as such "neural" and "machine learning" are hard to fine-tune and change. Thus, finding a way to evaluate them easily and diversely would greatly help the task of bettering them.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 11 references that are in the map. First posted on arXiv 1 Jun 2021.
Builds on
6 of 11 references.
References · 6
- 2017Six Challenges for Neural Machine Translationcited by 334
- 2018BLEU is Not Suitable for the Evaluation of Text Simplificationcited by 78
- 2016How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairscited by 39
- 2017A Challenge Set Approach to Evaluating Machine Translationcited by 37
- 2019Automatically Extracting Challenge Sets for Non local Phenomena in Neural Machine Translationcited by 15
- 2020Fine-Grained Analysis of Cross-Linguistic Syntactic Divergencescited by 9
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Citing papers per year: 2021: 1.
1 paper.