Intelligent Translation Memory Matching and Retrieval with Sentence Encoders
Table 10: Mean STS score for the sentences retrieved by Universal Sentence Encoder for each fuzzy match score. Fuzzy score column shows the fuzzy match score ranges and Mean STS score column shows that mean STS score for the sentence retrieved by Universal Sentence Encoder for that fuzzy match score range.
['[BOLD] Fuzzy score', '[BOLD] Mean STS score']
In this paper we have proposed a new TM matching and retrieval method based on the Universal Sentence Encoder. Our assumption was that by using this representation we will be able to retrieve better segments from a TM than when using a standard edit distance. Therefore, we believe that the sentence encoders can improve the matching and retrieval in TMs and should be explored more. Usually TM matches with lower fuzzy match scores (¡ 0.8) are not used by professional translators, or when used, they lead to a decrease in translation productivity. But our method can provide better matches to sentences below fuzzy match score 0.8, hence will be able to improve the translation productivity. According to the annotation guidelines of [Cer2017SemEval2017T1] a semantic textual similarity score of 0.8 means ”The two sentences are mostly equivalent, but some unimportant details differ” and semantic textual similarity score of 0.6 means ”The two sentences are roughly equivalent, but some important information differs/missing”. the mean semantic textual similarity for the sentences provided by Universal Sentence Encoder is 0.768. Therefore, we assume that the matches retrieved from the Universal Sentence Encoder in the fuzzy match score range 0.6-0.8 will help to improve the translation productivity. However, this is something that we plan to analyse further by carrying out evaluations with professional translators.