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|
Our approach, Text matching with Deep Info Max (TIM), is integrated with a procedure of unsupervised learning of representations by maximizing the mutual information between text matching neural network's input and output.
Many recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by merely looking at the hypothesis while completely ignoring the premise.
Learning what to share between tasks has been a topic of great importance recently, as strategic sharing of knowledge has been shown to improve the performance of downstream tasks.
We present PrecisionBatching, a quantized inference algorithm for speeding up neural network execution on traditional hardware platforms at low bitwidths without the need for retraining or recalibration.
With a single GPU, our approach can obtain a foreign BERT base model within a day and a foreign BERT large within two days.
There has been significant progress in recent years in the field of Natural Language Processing thanks to the introduction of the Transformer architecture.
Aspect based sentiment analysis aims to identify the sentimental tendency towards a given aspect in text.
Our experiments show that as a result of the substantial reduction of these biases, models trained on the filtered datasets yield better generalization to out-of-distribution tasks, especially when the benchmarks used for training are over-populated with biased samples.
Some NLP tasks can be solved in a fully unsupervised fashion by providing a pretrained language model with "task descriptions" in natural language (e. g., Radford et al., 2019).