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

TL;DR

This paper introduces iMATCH, an ILP-based algorithm for interpretable semantic textual similarity, aligning multiple non-contiguous chunks and assigning similarity scores, demonstrating superior performance and efficiency on multiple datasets.

Contribution

The paper presents a novel ILP-based algorithm for multi-chunk alignment and a supervised classification approach for similarity scoring, enhancing interpretability in semantic textual similarity tasks.

Findings

iMATCH outperforms most systems in alignment score

The system is efficient with low execution time

Achieved top scores on specific datasets

Abstract

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.