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Then we design a multi-task neural network model to incorporate our BiSynData to benefit implicit discourse relation recognition.
Also based on PDTB classification, Huang et al. (2014) adopted discourse connectives to reveal explicit discourse relation in Chinese.
Although SynData has been proven useful for implicit discourse relation recognition, it also has the meaning shift problem and the domain problem.
The three different types of discourse relation are circled in Figure 7 below, which is modeled on the kind of layout Gleason and his students favored for reticula.
Moreover, the Chinese discourse connectives have a variety of parts of speech that further deepen the difficulties of Chinese discourse relation labeling.
They claimed promising results on identifying a discourse relation; classifying the semantic type of explicit, implicit, or altLex; and determining the argument span.
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Recognizing implicit discourse relations is an important but challenging task in discourse understanding.
As given above, many classification systems for discourse relations had been established and experiments on different aspects of discourse relations were carried out.
Other areas where a situation semantics perspective has led to progress include attitude ascriptions, questions, tense, aspect, nominalizations, implicit arguments, point of view, counterfactual conditionals, and discourse relations.
Therefore, semantic relations have a broader coverage than head-dependent relations, since other than head-dependent relations, they also include coordinate relations and discourse relations.
Such a simple schema annotates only the semantic role of dependent daughter of head-dependent relations without considering coordinate relations and relations across sentences, such as discourse relations.
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