Papers with Code 아카이브

Natural Language Inference

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 3
평가표: 43
보존된 평가 기록: 505

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:

Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/cheng18/bilm-tf

Deep contextualized word representations

https://github.com/chesterdu/contrastive_summary

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/chiayewken/bert-qa

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

https://github.com/chrisc36/bottom-up-attention-vqa

Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases

https://github.com/chrisleunglokhin/Capstone-BERT

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

https://github.com/chrisociepa/allamo

PaLM: Scaling Language Modeling with Pathways

https://github.com/chujiezheng/ChID-Dataset

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

https://github.com/chunghyunhee/twitter_disaster_NLP

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

https://github.com/chunhuililili/mt_dnn

Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding

https://github.com/circlePi/BERT_Chinese_Text_Class_By_pytorch

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

https://github.com/cliang1453/camero

SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization

https://github.com/clips/gsoc2019_bias

Generating Natural Language Adversarial Examples

https://github.com/cloneofsimo/RealFormer-pytorch

RealFormer: Transformer Likes Residual Attention

https://github.com/clovaai/textual-kd-slu

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/cluebenchmark/simclue

SimCSE: Simple Contrastive Learning of Sentence Embeddings

https://github.com/cmunnis/BERT_vs_Transformer-XL

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

https://github.com/coco60/bert-test

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

https://github.com/codedecde/Recognizing-Textual-Entailment

Reasoning about Entailment with Neural Attention

https://github.com/codedotal/gpt-code-clippy

Language Models are Few-Shot Learners

https://github.com/codegram/calbert

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

https://github.com/codertimo/BERT-pytorch

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

https://github.com/codexxxl/GraphVQA

LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question Answering

https://github.com/codykala/ABCNN

ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

https://github.com/coetaur0/ESIM

Enhanced LSTM for Natural Language Inference

https://github.com/colinzhaoust/intrinsic_fewshot_hardness

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

https://github.com/common-english/bert-all

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

https://github.com/conceptofmind/LaMDA-pytorch

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

https://github.com/conceptofmind/PaLM-flax

PaLM: Scaling Language Modeling with Pathways

https://github.com/conceptofmind/lamda-rlhf-pytorch

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

https://github.com/contextlab/abstract2paper

Language Models are Few-Shot Learners

https://github.com/coronazap/bert_client

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

https://github.com/cosmoquester/2021-dialogue-summary-competition

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

https://github.com/cosmoquester/transformers-bart-pretrain

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

https://github.com/cospplay/bert-master

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

https://github.com/cove-adml/adml-anon

Learned in Translation: Contextualized Word Vectors

https://github.com/crazydigger/Callibration-of-GPT

Language Models are Few-Shot Learners

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.