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We describe our contribution to the CoNLL 2016 Shared Task on shallow discourse parsing.1 Our system extends the two best parsers from previous year’s competition by integration of a novel implicit sense labeling component. It is grounded on a highly generic, language-independent feedforward neural network architecture incorporating weighted word embeddings for argument spans which obviates the need for (traditional) hand-crafted features. Despite its simplicity, our system overall outperforms all results from 2015 on 5 out of 6 evaluation sets for English and achieves an absolute improvement in F1-score of 3.2% on the PDTB test section for non-explicit sense classification.

Do We Really Need All Those Rich Linguistic Features? A Neural Network-Based Approach to Implicit Sense Labeling