However, a single model is still typically trained and deployed for each task separately, requiring labeled training data for each, which makes it challenging to support new tasks, even within a single business vertical (e. g., food-ordering or travel booking).
The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders.
We investigate how well BERT performs on predicting factuality in several existing English datasets, encompassing various linguistic constructions.
Natural language inference (NLI) datasets (e. g., MultiNLI) were collected by soliciting hypotheses for a given premise from annotators.
Here, we explore the hypothesis that linguistic deficits drive the error patterns of existing speaker commitment models by analyzing the linguistic correlates of model error on a challenging naturalistic dataset.
This paper describes our system submission to the 2018 Fact Extraction and VERification (FEVER) shared task.