Pre-meeting (Dong Nguyen).
Related paper: Discourse Connective Argument Identification with Connective Specific Rankers
Focus paper: Genre Distinctions for Discourse in the Penn Treebank
The goal of the related paper is to automatically identify the arguments of discourse connectives ('and', 'however' etc). Previous research looked at training a single classifier for this task. The authors in this paper argue that connectives differ, and thus to be effective, it is better to model the individual connectives.
Their approach trains models for specific connectives, but to overcome the fewer amounts of training data available for each connective individually, they interpolate this with more global models. Specifically they interpolated with a model trained over all connectives together and models trained over connective types ( they divided all connectives into one of three types). They used a maximum entropy ranker and used the Penn Discourse Treebank dataset. Results showed improved when using the interpolation model.
In addition to this new interpolation approach, they introduced new features that looked at syntactic, morphology and relation with other connectives.
Overall, I think it was a nice paper. Good idea and nice evaluation.