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We present FewRel 2. 0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of instances?
In particular, we explore different ways of integrating the named entity types of the relation arguments into a neural network for relation classification, including a joint training and a structured prediction approach.
We formulate argumentative relation classification (support vs. attack) as a text-plausibility ranking task.
Automatically classifying the relation between sentences in a discourse is a challenging task, in particular when there is no overt expression of the relation.
Prior work on temporal relation classification has focused extensively on event pairs in the same or adjacent sentences (local), paying scant attention to discourse-level (global) pairs.
Discourse relation classification has proven to be a hard task, with rather low performance on several corpora that notably differ on the relation set they use.
Word pairs across argument spans have been shown to be effective for predicting the discourse relation between them.
Triplets extraction is an essential and pivotal step in automatic knowledge base construction, which captures structural information from unstructured text corpus.