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Natural Language Inference

109 papers with code · Natural Language Processing

Natural language inference is the task of determining whether a "hypothesis" is true (entailment), false (contradiction), or undetermined (neutral) given a "premise".

Example:

Premise Label Hypothesis
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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Greatest papers with code

Deep contextualized word representations

HLT 2018 zalandoresearch/flair

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).

CITATION INTENT CLASSIFICATION COREFERENCE RESOLUTION LANGUAGE MODELLING NAMED ENTITY RECOGNITION (NER) NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS

A Structured Self-attentive Sentence Embedding

9 Mar 2017jadore801120/attention-is-all-you-need-pytorch

This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention.

NATURAL LANGUAGE INFERENCE SENTENCE EMBEDDING SENTIMENT ANALYSIS

Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

ICLR 2018 facebookresearch/InferSent

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.

MULTI-TASK LEARNING NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION SEMANTIC TEXTUAL SIMILARITY

SentEval: An Evaluation Toolkit for Universal Sentence Representations

LREC 2018 facebookresearch/InferSent

We introduce SentEval, a toolkit for evaluating the quality of universal sentence representations.

NATURAL LANGUAGE INFERENCE

Improving Language Understanding by Generative Pre-Training

Preprint 2018 openai/finetune-transformer-lm

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.

DOCUMENT CLASSIFICATION LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY