Incorporating Paraphrasing in Translation Memory Matching and Retrieval

Current Translation Memory (TM) systems work at the surface level and lack semantic knowledge while matching. This paper presents an approach to incorporating semantic knowledge in the form of paraphrasing in matching and retrieval. Most of the TMs use Levenshtein editdistance or some variation of it. Generating additional segments based on the paraphrases available in a segment results in exponential time complexity while matching. The reason is that a particular phrase can be paraphrased in several ways and there can be several possible phrases in a segment which can be paraphrased. We propose an efficient approach to incorporating paraphrasing with edit-distance. The approach is based on greedy approximation and dynamic programming. We have obtained significant improvement in both retrieval and translation of retrieved segments for TM thresholds of 100%, 95% and 90%.