Without discourse connectives, classifying implicit discourse relations is a challenging task and a bottleneck for building a practical discourse parser. Previous research usually makes use of one kind of discourse framework such as PDTB or RST to improve the classification performance on discourse relations. Actually, under different discourse annotation frameworks, there exist multiple corpora which have internal connections. To exploit the combination of different discourse corpora, we design related discourse classification tasks specific to a corpus, and propose a novel Convolutional Neural Network embedded multi-task learning system to synthesize these tasks by learning both unique and shared representations for each task. The experimental results on the PDTB implicit discourse relation classification task demonstrate that our model achieves significant gains over baseline systems.
The supervised method often approaches discourse analysis as a classification problem of pairs of sentences arguments. The first work to tackle this task on PDTB were @cite_5 . They selected several surface features to train four binary classifiers, each for one of the top-level PDTB relation classes. Although other features proved to be useful, word pairs were the major contributor to most of these classifiers. Interestingly, they found that training these features on PDTB was more useful than training them on an external corpus. Extending from this work, lin2009recognizing (2009) further identified four different feature types representing the context, the constituent parse trees, the dependency parse trees and the raw text respectively. In addition, park2012improving (2012) promoted the performance through optimizing the feature set. Recently, mckeown2013aggregated (2013) tried to tackle the feature sparsity problem by aggregating features. rutherford-xue:2014:EACL (2014) used brown cluster to replace the word pair features, achieving the state-of-the-art classification performance. TACL536 used two recursive neural networks to represent the arguments and the entity spans and use the combination of the representations to predict the discourse relation.
@cite_5
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We present a series of experiments on automatically identifying the sense of implicit discourse relations, i.e. relations that are not marked with a discourse connective such as "but" or "because". We work with a corpus of implicit relations present in newspaper text and report results on a test set that is representative of the naturally occurring distribution of senses. We use several linguistically informed features, including polarity tags, Levin verb classes, length of verb phrases, modality, context, and lexical features. In addition, we revisit past approaches using lexical pairs from unannotated text as features, explain some of their shortcomings and propose modifications. Our best combination of features outperforms the baseline from data intensive approaches by 4 for comparison and 16 for contingency.