SlowGuess/ACL_2020_OCR huggingface dataset:
MNLI The Multi-Genre Natural Language Inference Corpus (Williams et al., 2018) is a collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral) and is evaluated by accuracy on both matched (in-domain) and mismatched (cross-domain) sections of the test data.
QNLI The Question-answering NLI dataset is converted from the Stanford Question Answering Dataset (SQuAD) (Rajpurkar et al., 2016). The task is to determine whether the context sentence contains the answer to the question and is evaluated by the test accuracy.
RTE The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges (Bentivogli et al., 2009). The task is to predict whether sentences in a sentence pair are entailment and is evaluated by accuracy.
WNLI The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun
from a list of choices. We follow Devlin et al. (2018) to skip this task in our experiments, because few previous works do better than predicting the majority class for this task.
<sup>8</sup>https://data.quora.com/ First-Quora-Dataset-Release-Question-Pairs