A novel automatic evaluation metric for Machine Translation based on word embeddings.

STD incorporates both semantic and lexical features (word embeddings & n-gram & word order) into one metric. It measures the semantic distance between the hypothesis and reference by calculating the minimum cumulative cost that the embedded n-grams of the hypothesis need to “travel” to reach the embedded n-grams of the reference. Experiment results show that STD has a better and more robust performance than a range of state-of-the-art metrics for both the segment-level and system-level evaluation.

python 3.6.5
pyemd, numpy, gensim, sklearn, nltk, jieba, codecs, re

Chinese pretrained model:
http://pan.baidu.com/s/1qX334vE
English pretrained model:
https://dl.fbaipublicfiles.com/fasttext/vectors-english/crawl-300d-2M.vec.zip

1. Set pretrained word embeddings model path in config.py
2. STD.py [-h] -r REF -o HYP -l LANG [-a1 ALPHA1] [-a2 ALPHA2]
              [-a3 ALPHA3] [-a4 ALPHA4] [-b1 BETA1] [-b2 BETA2] [-v]

STD: Semantic Travel Distance - An automatic evaluation metric for Machine
Translation.

optional arguments:
  -h, --help            show this help message and exit
  -r REF, --ref REF     Reference file
  -o HYP, --hyp HYP     Hypothesis file
  -l LANG, --lang LANG  Language: en for English; cn for Chinese
  -a1 ALPHA1, --alpha1 ALPHA1
                        Weight of unigram STD score at segment level (Default: 0.5)
  -a2 ALPHA2, --alpha2 ALPHA2
                        Weight of bigram STD score at segment level (Default: 0.5)
  -a3 ALPHA3, --alpha3 ALPHA3
                        Weight of unigram STD score at syetem level (Default: 0.3)
  -a4 ALPHA4, --alpha4 ALPHA4
                        Weight of bigram STD score at system level (Default: 0.7)
  -b1 BETA1, --beta1 BETA1
                        Weight of semantic distance matrix (Default: 0.6)
  -b2 BETA2, --beta2 BETA2
                        Weight of n-gram order distance matrix (Default: 0.4)
  -v, --verbose         Print score of each sentence (Default: False)