Papers with Code 아카이브
Natural Language Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/booydar/t5-experiments
공식 코드https://github.com/bothe/EDAs
공식 코드EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators
https://github.com/bothe/dialogue-act-recognition
공식 코드A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks
https://github.com/boun-tabi-lmg/turkish-lm-tuner
공식 코드TURNA: A Turkish Encoder-Decoder Language Model for Enhanced Understanding and Generation
https://github.com/bsantraigi/HIER
공식 코드https://github.com/bsantraigi/hier-transformer-pytorch
공식 코드https://github.com/cair/TextUnderstandingTsetlinMachine
공식 코드https://github.com/cambridgeltl/sqatin
공식 코드SQATIN: Supervised Instruction Tuning Meets Question Answering for Improved Dialogue NLU
https://github.com/canyuchen/promptda
공식 코드PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners
https://github.com/caoyu-noob/Multi-GPT2
공식 코드Pretrained Language Models for Dialogue Generation with Multiple Input Sources
https://github.com/cc-rmd-epibio/domain-adaptation-meta-analysis
공식 코드https://github.com/chadlzx/nsp_qa
공식 코드A Neural-Symbolic Approach to Natural Language Understanding
https://github.com/chaos96/fourierft
공식 코드Parameter-Efficient Fine-Tuning with Discrete Fourier Transform
https://github.com/chaoyi-wu/pmc-llama
공식 코드PMC-LLaMA: Towards Building Open-source Language Models for Medicine
https://github.com/chasonshi/melora
공식 코드MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning
https://github.com/chen-zichen/xplainllm_dataset
공식 코드XplainLLM: A Knowledge-Augmented Dataset for Reliable Grounded Explanations in LLMs
https://github.com/claudiacuttano/samwise
공식 코드SAMWISE: Infusing Wisdom in SAM2 for Text-Driven Video Segmentation
https://github.com/claws-lab/few-shot-adversarial-robustness
공식 코드Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding
https://github.com/cliang1453/sage
공식 코드https://github.com/clmbrs/communication-translation
공식 코드https://github.com/coastalcph/lex-glue
공식 코드LexGLUE: A Benchmark Dataset for Legal Language Understanding in English
https://github.com/cogcomp/event-linking
공식 코드https://github.com/cogcomp/multi-persp-search-engine
공식 코드https://github.com/cognitiveaisystems/3dgraphllm
공식 코드3DGraphLLM: Combining Semantic Graphs and Large Language Models for 3D Scene Understanding
https://github.com/coldmist-lu/erroranalysis_prompt
공식 코드Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models
https://github.com/coling2022-depro/depro
공식 코드https://github.com/cooelf/SemBERT
공식 코드https://github.com/cooelf/instancereg
공식 코드Instance Regularization for Discriminative Language Model Pre-training
https://github.com/copenlu/X-MAML
공식 코드https://github.com/copenlu/spanex
공식 코드https://github.com/csarkar/tage
공식 코드tagE: Enabling an Embodied Agent to Understand Human Instructions
https://github.com/csebuetnlp/banglabert
공식 코드https://github.com/csinva/imodelsX
공식 코드Explaining Patterns in Data with Language Models via Interpretable Autoprompting
https://github.com/csitfun/glore
공식 코드GLoRE: Evaluating Logical Reasoning of Large Language Models
https://github.com/csitfun/logieval
공식 코드Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.