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Found 9 papers, 1 papers with code
Multi-Task Learning for Joint Semantic Role and Proto-Role Labeling
Unlike previous work, our model does not require pre-training or fine-tuning on additional tasks, beyond using off-the-shelf (static or contextual) embeddings and supervision.
Where's the Learning in Representation Learning for Compositional Semantics and the Case of Thematic Fit
Observing that for certain NLP tasks, such as semantic role prediction or thematic fit estimation, random embeddings perform as well as pretrained embeddings, we explore what settings allow for this and examine where most of the learning is encoded: the word embeddings, the semantic role embeddings, or ``the network''.
Thematic Fit Bits: Annotation Quality and Quantity Interplay for Event Participant Representation
We compare the old and new corpus versions' impact on a verb--argument fit modeling task, using a high-performing neural approach.
E-TIPSY: Search Query Corpus Annotated with Entities, Term Importance, POS Tags, and Syntactic Parses
In addition, it contains automatically produced annotations of named entities, part-of-speech tags, and syntactic parses for the same queries.
