Natural Language Inference

714 papers with code • 43 benchmarks • 77 datasets

Natural language inference (NLI) 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.

Approaches used for NLI include earlier symbolic and statistical approaches to more recent deep learning approaches. Benchmark datasets used for NLI include SNLI, MultiNLI, SciTail, among others. You can get hands-on practice on the SNLI task by following this d2l.ai chapter.

Further readings:

Libraries

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Most implemented papers

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

google-research/bert NAACL 2019

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.

RoBERTa: A Robustly Optimized BERT Pretraining Approach

pytorch/fairseq 26 Jul 2019

Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.

A Structured Self-attentive Sentence Embedding

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

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

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

huggingface/transformers arXiv 2019

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

google-research/ALBERT ICLR 2020

Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.

Deep contextualized word representations

flairNLP/flair NAACL 2018

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).

Language Models are Few-Shot Learners

openai/gpt-3 NeurIPS 2020

By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

huggingface/transformers ACL 2020

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.

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

huggingface/transformers NeurIPS 2019

As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging.

XLNet: Generalized Autoregressive Pretraining for Language Understanding

zihangdai/xlnet NeurIPS 2019

With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.