Vivek Srikumar

Tao Li and Vivek Srikumar

Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016.

We study the problem of jointly aligning sentence constituents and predicting their similarities. While extensive sentence similarity data exists, manually generating reference alignments and labeling the similarities of the aligned chunks is comparatively onerous. This prompts the natural question of whether we can exploit easy-to-create sentence level data to train better aligners. In this paper, we present a model that learns to jointly align constituents of two sentences and also predict their similarities. By taking advantage of both sentence and constituent level data, we show that our model achieves state-of-the-art performance at predicting alignments and constituent similarities.

@inproceedings{li2016exploiting,
  author = {Li, Tao and Srikumar, Vivek},
  title = {{Exploiting Sentence Similarities for Better Alignments}},
  booktitle = {Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2016}
}