Implicit discourse relation recognition remains a challenging task as state-of-the-art approaches reach F1 scores ranging from 9.95% to 37.67% on the 2016 CoNLL shared task. In our work, we explore the use of a neural network which exploits the strong correlation between pairs of words across two discourse arguments that implicitly signal a discourse relation. We present a novel approach to Implicit Discourse Relation Recognition that uses an encoder-decoder model with attention. Our approach is based on the assumption that a discourse argument is "generated" from a previous argument and conditioned on a latent discourse relation, which we detect. Experiments show that our model achieves an F1 score of 38.25% on fine-grained classification, outperforming previous approaches and performing comparatively with state-of-the-art on coarse-grained classification, while computing alignment parameters without the need for additional pooling and fully connected layers.
@InProceedings{CIANFLONE18.451, author = {Andre Cianflone and Leila Kosseim}, title = "{Attention for Implicit Discourse Relation Recognition}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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