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|
New models for natural language understanding have made unusual progress recently, leading to claims of universal text representations.
Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks.
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
Attentional models are distinctly characterized by their ability to learn relative importance, i. e., assigning a different weight to input values.
Additionally, we show that the transfer step of TANDA makes the adaptation step more robust to noise.
SOTA for Question Answering on WikiQA
We measure the performance of CamemBERT compared to multilingual models in multiple downstream tasks, namely part-of-speech tagging, dependency parsing, named-entity recognition, and natural language inference.
The key idea of the proposed approach is to use a Forward Transformation to transform dense representations to sparse representations.
Moreover, it is shown that reasonable performance can be obtained when ZEN is trained on a small corpus, which is important for applying pre-training techniques to scenarios with limited data.
We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.
#8 best model for Question Answering on SQuAD1.1 dev (F1 metric)
Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents.