STD: An Automatic Evaluation Metric for Machine Translation Based on Word Embeddings
STD: An Automatic Evaluation Metric for Machine Translation Based on Word Embeddings
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STD:基于词嵌入的机器翻译自动评估指标
DOI:
10.1109/taslp.2019.2922845
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发表时间:
2019-10
期刊:
影响因子:
--
通讯作者:
Zheng Zibin
中科院分区:
文献类型:
--
作者:
Li Pairui;Chen Chuan;Zheng Wujie;Deng Yuetang;Ye Fanghua;Zheng Zibin
Lexical-based metrics such as BLEU, NIST, and WER have been widely used in machine translation (MT) evaluation. However, these metrics badly represent semantic relationships and impose strict identity matching, leading to moderate correlation with human judgments. In this paper, we propose a novel MT automatic evaluation metric Semantic Travel Distance (STD) based on word embeddings. STD incorporates both semantic and lexical features (word embeddings and n-gram and 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.
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