Natural language inference is the task of determining whether a "hypothesis" is true (entailment), false (contradiction), or undetermined (neutral) given a "premise".
|A man inspects the uniform of a figure in some East Asian country.||contradiction||The man is sleeping.|
|An older and younger man smiling.||neutral||Two men are smiling and laughing at the cats playing on the floor.|
|A soccer game with multiple males playing.||entailment||Some men are playing a sport.|
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Given a table and a statement/fact, subtask A determines whether the statement is inferred from the tabular data, and subtask B determines which cells in the table provide evidence for the former subtask.
The experimental results show that the proposed approach can generate reasonable explanations for its predictions even with a small-scale training explanation text.
Thinking aloud is an effective meta-cognitive strategy human reasoners apply to solve difficult problems.
Given that the agents as well as the customers can have varying levels of literacy, the overall quality of responses provided by the agents tend to be poor if they are not predefined.
NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand complex sentences and established a relation between a hypothesis and a premise.
In this paper, we explore multi-task learning (MTL) as a second pretraining step to learn enhanced universal language representation for transformer language models.
Sharing information between unrelated tasks might hurt performance, and it is unclear how to transfer knowledge across tasks with a hierarchical structure.
Modern natural language understanding models depend on pretrained subword embeddings, but applications may need to reason about words that were never or rarely seen during pretraining.
Recent works have demonstrated reasonable success of representation learning in hypercomplex space.
Our results suggest that SMS tasks decrease the average CGI ability of upper layers, while NLI tasks increase it.