Argument Labeling of Explicit Discourse Relations using LSTM Neural Networks

08/11/2017

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by   Sohail Hooda, et al.

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Argument labeling of explicit discourse relations is a challenging task. The state of the art systems achieve slightly above 55 hand-crafted features. In this paper, we propose a Long Short Term Memory (LSTM) based model for argument labeling. We experimented with multiple configurations of our model. Using the PDTB dataset, our best model achieved an F1 measure of 23.05 higher than the 20.52 significantly lower than the feature based state of the art systems. On the other hand, because our approach learns only from the raw dataset, it is more widely applicable to multiple textual genres and languages.