# Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings

arXiv: https://arxiv.org/abs/1704.00380 · HTML: https://mlsanity.com/paper/1704.00380/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2017.

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

- [RoMe: A Robust Metric for Evaluating Natural Language Generation](https://mlsanity.com/paper/2203.09183/) (2022)

## Abstract (arXiv)

One of the most important problems in machine translation (MT) evaluation is to evaluate the similarity between translation hypotheses with different surface forms from the reference, especially at the segment level. We propose to use word embeddings to perform word alignment for segment-level MT evaluation. We performed experiments with three types of alignment methods using word embeddings. We evaluated our proposed methods with various translation datasets. Experimental results show that our proposed methods outperform previous word embeddings-based methods.