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Interpretable semantic textual similarity (iSTS) task adds a crucialexplanatory layer to pairwise sentence similarity. We address variouscomponents of this task: chunk level semantic alignment along with assignmentof similarity type and score for aligned chunks with a novel system presentedin this paper. We propose an algorithm, iMATCH, for the alignment of multiplenon-contiguous chunks based on Integer Linear Programming (ILP). Similaritytype and score assignment for pairs of chunks is done using a supervisedmulticlass classification technique based on Random Forrest Classifier. Resultsshow that our algorithm iMATCH has low execution time and outperforms mostother participating systems in terms of alignment score. Of the three datasets,we are top ranked for answer- students dataset in terms of overall score andhave top alignment score for headlines dataset in the gold chunks track.

IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner