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Cross-lingual transfer learning for spoken language understanding
Typically, spoken language understanding (SLU) models are trained on annotated data which are costly to gather.
A Flexible and Easy-to-use Semantic Role Labeling Framework for Different Languages
This paper presents a flexible and open source framework for deep semantic role labeling.
Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments
Implicit semantic role labeling (iSRL) is the task of predicting the semantic roles of a predicate that do not appear as explicit arguments, but rather regard common sense knowledge or are mentioned earlier in the discourse.
Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training
We present a successful collaboration of word embeddings and co-training to tackle in the most difficult test case of semantic role labeling: predicting out-of-domain and unseen semantic frames.
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