https://github.com/HQ01/BOWMIAN
공식 코드Analysis of Bag-of-n-grams Representation's Properties Based on Textual Reconstruction
Papers with Code 아카이브
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/HSLCY/ABSA-BERT-pair
공식 코드Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
https://github.com/Hamoon1987/ABSA
공식 코드Exploiting BERT to improve aspect-based sentiment analysis performance on Persian language
https://github.com/HanNight/RE-T5
공식 코드https://github.com/HaoYang0123/Position-Focused-Attention-Network
공식 코드https://github.com/HareeshBahuleyan/probabilistic_nlg
공식 코드Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation
https://github.com/HarrywillDr/KeyPoint-Analysis
공식 코드https://github.com/HarshOza36/Detection-Of-Machine-Generated-Text
공식 코드https://github.com/Hazelsuko07/TextHide
공식 코드TextHide: Tackling Data Privacy in Language Understanding Tasks
https://github.com/Heidelberg-NLP/CGGC
공식 코드The Mystery of Compositional Generalization in Graph-based Generative Commonsense Reasoning
https://github.com/Heidelberg-NLP/COINS
공식 코드COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion
https://github.com/Heidelberg-NLP/MFscore
공식 코드Towards a Decomposable Metric for Explainable Evaluation of Text Generation from AMR
https://github.com/Heidelberg-NLP/discourse-aware-semantic-self-attention
공식 코드Discourse-Aware Semantic Self-Attention for Narrative Reading Comprehension
https://github.com/Heidelberg-NLP/simple-xamr
공식 코드Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsing
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/HeoTaksung/A-Novel-Hybrid-Methodology-of-Measuring-Sentence-Similarity
공식 코드https://github.com/Hossam-Mohammed-tech/Intent_Classification_OOS
공식 코드Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-Classification
https://github.com/Hrezaei/SummBot
공식 코드Features in Extractive Supervised Single-document Summarization: Case of Persian News
https://github.com/Huntersxsx/MGPN
공식 코드You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos
https://github.com/Huntersxsx/RaNet
공식 코드Relation-aware Video Reading Comprehension for Temporal Language Grounding
https://github.com/IAmS4n/TextGenerationEvaluationMetrics
공식 코드Jointly Measuring Diversity and Quality in Text Generation Models
https://github.com/IBM/GPT-too-AMR2text
공식 코드GPT-too: A language-model-first approach for AMR-to-text generation
https://github.com/IBM/LNN
공식 코드LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking
https://github.com/IBM/WordMoversEmbeddings
공식 코드https://github.com/IBM/span-selection-pretraining
공식 코드https://github.com/IBM/transition-amr-parser
공식 코드Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing
https://github.com/ICHEC/QNLP
공식 코드A hybrid classical-quantum workflow for natural language processing
https://github.com/IIT-PAVIS/Positional_Diffusion
공식 코드Positional Diffusion: Ordering Unordered Sets with Diffusion Probabilistic Models
https://github.com/IITB-OpenOCRCorrect/iitb-openocr-digit-tool
공식 코드UDAAN: Machine Learning based Post-Editing tool for Document Translation
https://github.com/IKMLab/arct
공식 코드NLITrans at SemEval-2018 Task 12: Transfer of Semantic Knowledge for Argument Comprehension
https://github.com/INK-USC/DualRE
공식 코드Learning Dual Retrieval Module for Semi-supervised Relation Extraction
https://github.com/INK-USC/NERO
공식 코드NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction
https://github.com/INK-USC/TriggerNER
공식 코드TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
https://github.com/INK-USC/XCSR
공식 코드https://github.com/ImKeTT/ZeroGen
공식 코드ZeroGen: Zero-shot Multimodal Controllable Text Generation with Multiple Oracles
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.