78 research outputs found
Semi-supervised multitask learning for sequence labeling
We propose a sequence labeling framework
with a secondary training objective,
learning to predict surrounding words
for every word in the dataset. This language
modeling objective incentivises the
system to learn general-purpose patterns
of semantic and syntactic composition,
which are also useful for improving accuracy
on different sequence labeling tasks.
The architecture was evaluated on a range
of datasets, covering the tasks of error
detection in learner texts, named entity
recognition, chunking and POS-tagging.
The novel language modeling objective
provided consistent performance improvements
on every benchmark, without requiring
any additional annotated or unannotated
data
Issue Framing in Online Discussion Fora
In online discussion fora, speakers often make arguments for or against
something, say birth control, by highlighting certain aspects of the topic. In
social science, this is referred to as issue framing. In this paper, we
introduce a new issue frame annotated corpus of online discussions. We explore
to what extent models trained to detect issue frames in newswire and social
media can be transferred to the domain of discussion fora, using a combination
of multi-task and adversarial training, assuming only unlabeled training data
in the target domain.Comment: To appear in NAACL-HLT 201
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