This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence. We also propose a self-attention mechanism and a special regularization term for the model. As a side effect, the embedding comes with an easy way of visualizing what specific parts of the sentence are encoded into the embedding. We evaluate our model on 3 different tasks: author profiling, sentiment classification, and textual entailment. Results show that our model yields a significant performance gain compared to other sentence embedding methods in all of the 3 tasks.
- Pub Date:
- March 2017
- Computer Science - Computation and Language;
- Computer Science - Artificial Intelligence;
- Computer Science - Machine Learning;
- Computer Science - Neural and Evolutionary Computing
- 15 pages with appendix, 7 figures, 4 tables. Conference paper in 5th International Conference on Learning Representations (ICLR 2017)