# Part of Speech and Universal Dependency effects on English Arabic Machine Translation

arXiv: https://arxiv.org/abs/2106.00745 · HTML: https://mlsanity.com/paper/2106.00745/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [BLEU is Not Suitable for the Evaluation of Text Simplification](https://mlsanity.com/paper/1810.05995/) (2018)
- [How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs](https://mlsanity.com/paper/1612.04629/) (2016)
- [A Challenge Set Approach to Evaluating Machine Translation](https://mlsanity.com/paper/1704.07431/) (2017)
- [Automatically Extracting Challenge Sets for Non local Phenomena in Neural Machine Translation](https://mlsanity.com/paper/1909.06814/) (2019)
- [Fine-Grained Analysis of Cross-Linguistic Syntactic Divergences](https://mlsanity.com/paper/2005.03436/) (2020)

## Built on by (most cited first, 1 of 1)

- [On the Relation between Syntactic Divergence and Zero-Shot Performance](https://mlsanity.com/paper/2110.04644/) (2021)

## 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.