Papers with Code 아카이브
Natural Language Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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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/xingrun-xing/bipft
공식 코드https://github.com/xingwei-warwick/hyper-event-temprel
공식 코드https://github.com/xliuhw/NLU-Evaluation-Data
공식 코드Benchmarking Natural Language Understanding Services for building Conversational Agents
https://github.com/xlxwalex/hycxg
공식 코드Enhancing Language Representation with Constructional Information for Natural Language Understanding
https://github.com/xu-song/k-plug
공식 코드https://github.com/xuanlinli17/large_vlm_distillation_ood
공식 코드Distilling Large Vision-Language Model with Out-of-Distribution Generalizability
https://github.com/xxxiaol/spatial-commonsense
공식 코드Things not Written in Text: Exploring Spatial Commonsense from Visual Signals
https://github.com/xzy-xzy/dedc
공식 코드https://github.com/yakazimir/Code-Datasets
공식 코드https://github.com/yale-lily/folio
공식 코드https://github.com/yanglinyi/glue-x
공식 코드https://github.com/yanglinyi/supervised-knowledge-makes-large-language-models-better-in-context-learners
공식 코드Supervised Knowledge Makes Large Language Models Better In-context Learners
https://github.com/yasumasaonoe/creak
공식 코드CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge
https://github.com/ychen-stat-ml/kernel-adapters
공식 코드https://github.com/yflyl613/tiny-newsrec
공식 코드Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation
https://github.com/yinxusen/learning_to_generalize
공식 코드https://github.com/yitu-opensource/ConvBert
공식 코드ConvBERT: Improving BERT with Span-based Dynamic Convolution
https://github.com/ymcui/pert
공식 코드https://github.com/youganglyu/dct
공식 코드https://github.com/young1993/tlm
공식 코드https://github.com/yuanhy1997/genbioel
공식 코드https://github.com/yujielu10/iace-nlu
공식 코드https://github.com/yukiar/TransferFT
공식 코드https://github.com/yumeng5/supergen
공식 코드Generating Training Data with Language Models: Towards Zero-Shot Language Understanding
https://github.com/yxli2123/loftq
공식 코드LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
https://github.com/yxuansu/tacl
공식 코드TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning
https://github.com/zanyarz/zodiac
공식 코드https://github.com/zbambergerNLP/depth
공식 코드DEPTH: Discourse Education through Pre-Training Hierarchically
https://github.com/zerohd4869/SPC
공식 코드https://github.com/zerohd4869/infomtl
공식 코드https://github.com/zeronlp/consistentee
공식 코드https://github.com/zhangzhao219/wsdm-cup-2024
공식 코드https://github.com/zhaochaocs/narrasum
공식 코드NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization
https://github.com/zhou-cybersecurity-ai/synghost
공식 코드SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic Transfer
https://github.com/zhouj8553/flipda
공식 코드FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.