https://github.com/HSLCY/ABSA-BERT-pair
공식 코드Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
Papers with Code 아카이브
Natural Language Inference
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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:
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Hamoon1987/ABSA
공식 코드Exploiting BERT to improve aspect-based sentiment analysis performance on Persian language
https://github.com/HazyResearch/metal
공식 코드https://github.com/Heidelberg-NLP/MHKA
공식 코드Social Commonsense Reasoning with Multi-Head Knowledge Attention
https://github.com/Helsinki-NLP/HBMP
공식 코드Sentence Embeddings in NLI with Iterative Refinement Encoders
https://github.com/Helsinki-NLP/nli-data-sanity-check
공식 코드NLI Data Sanity Check: Assessing the Effect of Data Corruption on Model Performance
https://github.com/HenryJunW/TAG
공식 코드TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation
https://github.com/HiyaToki/Uncovering-Agendas
공식 코드Uncovering Agendas: A Novel French & English Dataset for Agenda Detection on Social Media
https://github.com/HomeroRR/rmm
공식 코드https://github.com/HooRin/AuditWen
공식 코드https://github.com/INK-USC/HGN
공식 코드Learning Contextualized Knowledge Structures for Commonsense Reasoning
https://github.com/INK-USC/KagNet
공식 코드KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
https://github.com/IntelLabs/academic-budget-bert
공식 코드https://github.com/JRC1995/Continuous-RvNN
공식 코드Modeling Hierarchical Structures with Continuous Recursive Neural Networks
https://github.com/Jacobsolawetz/large-scale-oie
공식 코드LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction
https://github.com/JeanKaddour/WASAM
공식 코드https://github.com/JinYuanLi0012/RiVEG
공식 코드LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition
https://github.com/KLUE-benchmark/KLUE
공식 코드https://github.com/KRR-Oxford/DeepOnto
공식 코드https://github.com/KomeijiForce/Active_Passive_Constraint_Koishiday_2024
공식 코드Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing
https://github.com/LeonChengg/EGs-tuned-LLMs
공식 코드https://github.com/LuanaBulla/Detection-of-Morality-in-Text
공식 코드Do Language Models Understand Morality? Towards a Robust Detection of Moral Content
https://github.com/LuisPB7/fnc-msc
공식 코드https://github.com/Maluuba/gensen
공식 코드Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
https://github.com/McGill-NLP/latent-translation
공식 코드https://github.com/MostHumble/PatientTrajectoryForecasting
공식 코드Patient Trajectory Prediction: Integrating Clinical Notes with Transformers
https://github.com/NervanaSystems/nlp-architect/blob/master/nlp_architect/models/transformers/quantized_bert.py
공식 코드https://github.com/OanaMariaCamburu/e-SNLI
공식 코드Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
https://github.com/PKUAI-LINGroup/PAS
공식 코드https://github.com/PaddlePaddle/ERNIE
공식 코드ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
https://github.com/PaddlePaddle/Paddle
공식 코드https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-1.0
공식 코드ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/PaddlePaddle/TrustAI/tree/main/tutorials/data_bias_identification/less_learn_shortcut
공식 코드Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label Correlation
https://github.com/PhoebusSi/SAR
공식 코드Check It Again: Progressive Visual Question Answering via Visual Entailment
https://github.com/Pzoom522/L1-Refinement
공식 코드Cross-Lingual Word Embedding Refinement by $\ell_{1}$ Norm Optimisation
https://github.com/QuintinPope/CLOSS
공식 코드Text Counterfactuals via Latent Optimization and Shapley-Guided Search
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.