IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner
This work addresses the need for interpretable similarity measures in NLP, but it is incremental as it builds on existing iSTS frameworks with a novel alignment method.
The paper tackled the interpretable semantic textual similarity (iSTS) task by developing iMATCH, an algorithm using Integer Linear Programming for multiple chunk alignment and Random Forest for similarity scoring, which achieved top alignment scores and low execution time on specific datasets.
Interpretable semantic textual similarity (iSTS) task adds a crucial explanatory layer to pairwise sentence similarity. We address various components of this task: chunk level semantic alignment along with assignment of similarity type and score for aligned chunks with a novel system presented in this paper. We propose an algorithm, iMATCH, for the alignment of multiple non-contiguous chunks based on Integer Linear Programming (ILP). Similarity type and score assignment for pairs of chunks is done using a supervised multiclass classification technique based on Random Forrest Classifier. Results show that our algorithm iMATCH has low execution time and outperforms most other participating systems in terms of alignment score. Of the three datasets, we are top ranked for answer- students dataset in terms of overall score and have top alignment score for headlines dataset in the gold chunks track.