Constructing a tur corpus for paraphrase identification and semantic similarity

The Paraphrase identification (PI) task has practical importance for work in Natural Language Processing (NLP) because of the problem of linguistic variation. Accurate methods should help improve performance of key NLP applications. Paraphrase corpora are important resources in developing and evaluating PI methods. This paper describes the construction of a paraphrase corpus for tur. The corpus comprises pairs of sentences with semantic similarity scores based on human judgments, permitting experimentation with both PI and semantic similarity. We believe this is the first such corpus for tur. The data collection and scoring methodology is described and initial PI experiments with the corpus are reported. Our approach to PI is novel in using ‘knowledge lean’ methods (i.e. no use of manually constructed knowledge bases or processing tools that rely on these). We have previously achieved excellent results using such techniques on the Microsoft Research Paraphrase Corpus, and close to state-of-the-art performance on the Twitter Paraphrase Corpus. © Springer International Publishing AG, part of Springer Nature 2018