Papers with Code 아카이브
Natural Language Understanding
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Natural Language Understanding is an important field of Natural Language Processing which contains various tasks such as text classification, natural language inference and story comprehension. Applications enabled by natural language understanding range from question answering to automated reasoning.
Source: Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Ruifeng-paper/FactExsum-coling2020
공식 코드Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT
https://github.com/RussianNLP/RussianSuperGLUE
공식 코드RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
https://github.com/SPFlow/SPFlow
공식 코드https://github.com/SaFoLab-WISC/OET
공식 코드https://github.com/SgfdDttt/sara
공식 코드A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering
https://github.com/ShakyaJayakody/EdgeProfiler
공식 코드EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical Model
https://github.com/SimiaoZuo/Caching-Adv
공식 코드ARCH: Efficient Adversarial Regularized Training with Caching
https://github.com/SimiaoZuo/Stackelberg-Adv
공식 코드Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach
https://github.com/StuartMesham/low_resource_lm
공식 코드https://github.com/TAU-VAILab/isbertblind
공식 코드https://github.com/THU-KEG/MRCEval
공식 코드MRCEval: A Comprehensive, Challenging and Accessible Machine Reading Comprehension Benchmark
https://github.com/THUDM/FewNLU
공식 코드FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding
https://github.com/THUDM/GLM
공식 코드GLM: General Language Model Pretraining with Autoregressive Blank Infilling
https://github.com/THUDM/P-tuning
공식 코드https://github.com/TRUMANCFY/SLIM
공식 코드SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling
https://github.com/TracyGuo2001/NLoRA
공식 코드NLoRA: Nyström-Initiated Low-Rank Adaptation for Large Language Models
https://github.com/UKPLab/acl2020-confidence-regularization
공식 코드Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance
https://github.com/UKPLab/coref-reasoning-in-qa
공식 코드https://github.com/ValentinaPy/QADiscourse
공식 코드QADiscourse -- Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines
https://github.com/VinAIResearch/JointIDSF
공식 코드https://github.com/Wellesley-EASEL-lab/GlyphPattern
공식 코드GlyphPattern: An Abstract Pattern Recognition for Vision-Language Models
https://github.com/XMUNLP/Tagger
공식 코드https://github.com/XuesongYang/end2end_dialog
공식 코드End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager
https://github.com/Xujxyang/Fairness-NC-main
공식 코드https://github.com/Yorko/gender-unbiased_BERT-based_pronoun_resolution
공식 코드https://github.com/ZhaofengWu/SIFT
공식 코드https://github.com/ZhaoqLiu/BreakNLI
공식 코드Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models
https://github.com/ZovanZhou/MLRN
공식 코드https://github.com/aaronlifenghan/swed_covid_tm
공식 코드https://github.com/aashish2000/anchor
공식 코드ANCHOR: LLM-driven News Subject Conditioning for Text-to-Image Synthesis
https://github.com/aatlantise/punc-rest-improves
공식 코드Punctuation Restoration Improves Structure Understanding Without Supervision
https://github.com/abdoelsayed2016/legal-question-answering-review
공식 코드https://github.com/abhilasharavichander/condaqa
공식 코드CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation
https://github.com/abisee/story-generation-eval
공식 코드Do Massively Pretrained Language Models Make Better Storytellers?
https://github.com/abteen/americasnli
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.