Nothing Special   »   [go: up one dir, main page]

×
Please click here if you are not redirected within a few seconds.
Apr 16, 2021 · Using natural language explanations, we supervise the model's attention weights to encourage more attention to be paid to the words present in ...
Sep 5, 2024 · Training with the human explanations encourages models to attend more broadly across the sentences, paying more attention to words in the ...
Using natural language explanations, we supervise a model's attention weights to encourage more attention to be paid to the words present in these explanations.
People also ask
Dec 4, 2018 · The Stanford Natural Language Inference dataset is extended with an additional layer of human-annotated natural language explanations of the entailment ...
In this work, we extend the Stanford Natural Language Inference dataset with an additional layer of human-annotated natural language explanations of the ...
Analysis of the model indi- cates that human explanations encourage increased attention on the important words, with more attention paid to words in the premise ...
Missing: Guide | Show results with:Guide
Using natural language explanations, supervised models are taught how a human would approach the NLI task, in order to learn features that will generalise ...
Missing: Touch: | Show results with:Touch:
In this work, we extend the Stanford Natural Language Inference dataset with an additional layer of human-annotated natural language explanations of the ...
Natural language inference (NLI) is the task of determining whether a "hypothesis" is true (entailment), false (contradiction), or undetermined (neutral) given ...
Missing: Touch: Guide
We denote the human-provided gold explanation for the correct predictions as tg. S denotes a module which predicts label scores. The true label for an example ...
Missing: Guide | Show results with:Guide