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.|
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We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
SOTA for Common Sense Reasoning on SWAG
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).
#2 best model for Coreference Resolution on CoNLL 2012
Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
DOMAIN ADAPTATION MACHINE TRANSLATION NAMED ENTITY RECOGNITION (NER) NATURAL LANGUAGE INFERENCE QUESTION ANSWERING RELATION EXTRACTION SEMANTIC PARSING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING
Finally, we introduce a new test set of aligned sentences in 122 languages based on the Tatoeba corpus, and show that our sentence embeddings obtain strong results in multilingual similarity search even for low-resource languages.
CROSS-LINGUAL BITEXT MINING CROSS-LINGUAL DOCUMENT CLASSIFICATION CROSS-LINGUAL NATURAL LANGUAGE INFERENCE CROSS-LINGUAL TRANSFER DOCUMENT CLASSIFICATION JOINT MULTILINGUAL SENTENCE REPRESENTATIONS PARALLEL CORPUS MINING
In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model.
We introduce SentEval, a toolkit for evaluating the quality of universal sentence representations.
Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.
We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task.
#3 best model for Natural Language Inference on SNLI