https://github.com/amazon-science/summarization-sicf-score
공식 코드Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection
Papers with Code 아카이브
Natural Language Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 6
- 보존된 평가 기록
- 42
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/amazon-science/vigor
공식 코드ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward Modeling
https://github.com/amazon-science/zero-shot-e2e-slu
공식 코드Zero-Shot End-to-End Spoken Language Understanding via Cross-Modal Selective Self-Training
https://github.com/amirabaskohi/peach
공식 코드https://github.com/amritkromana/disfluency_detection_from_audio
공식 코드https://github.com/andhmak/rule_dialnorm
공식 코드Dialect Normalization using Large Language Models and Morphological Rules
https://github.com/andy194673/Joint-NLU-NLG
공식 코드A Generative Model for Joint Natural Language Understanding and Generation
https://github.com/apairofbigwings/cross-lingual-opinion-mining
공식 코드A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion Mining
https://github.com/aquastar/csur_bridge_spectral_spatial_gnn_survey
공식 코드Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
https://github.com/ariecattan/CoRefi
공식 코드SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts
https://github.com/ariecattan/SciCo
공식 코드SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts
https://github.com/arminazizi98/lamda
공식 코드LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation
https://github.com/arranger1044/awesome-spn
공식 코드https://github.com/asaparov/fictionalgeoqa
공식 코드Towards General Natural Language Understanding with Probabilistic Worldbuilding
https://github.com/asaparov/grammar
공식 코드https://github.com/asaparov/parser
공식 코드https://github.com/asaparov/pwl
공식 코드Towards General Natural Language Understanding with Probabilistic Worldbuilding
https://github.com/ashi-ta/speechglue
공식 코드SpeechGLUE: How Well Can Self-Supervised Speech Models Capture Linguistic Knowledge?
https://github.com/asinghcsu/agenticrag-survey
공식 코드Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
https://github.com/aub-mind/Arabic-Empathetic-Chatbot
공식 코드Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little Data
https://github.com/avinashsai/mml
공식 코드https://github.com/avinashsai/slot-gpt
공식 코드A Unified Framework for Slot based Response Generation in a Multimodal Dialogue System
https://github.com/aws/aws-refcocog-adv
공식 코드https://github.com/bach1292/xagent
공식 코드From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent
https://github.com/baidu/lac
공식 코드https://github.com/baudzhou/wyweb
공식 코드https://github.com/beinborn/relative_importance
공식 코드https://github.com/bergen/edgetransformer
공식 코드https://github.com/bert-nmt/bert-nmt
공식 코드https://github.com/bgGLUE/bgglue
공식 코드bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark
https://github.com/bgglue/bgglue.github.io
공식 코드bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark
https://github.com/bigcat2333/derc
공식 코드A Simple yet Effective Self-Debiasing Framework for Transformer Models
https://github.com/bionlu-coling2024/biomed-ner-intent_detection
공식 코드Intent Detection and Entity Extraction from BioMedical Literature
https://github.com/blacknoodle/tucore-gcn
공식 코드Graph Based Network with Contextualized Representations of Turns in Dialogue
https://github.com/blester125/global-local-model
공식 코드https://github.com/bluezeros/medcare
공식 코드MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge Aggregation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.