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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/marzenakrp/nocha
공식 코드One Thousand and One Pairs: A "novel" challenge for long-context language models
https://github.com/marziehf/DataAugmentationNMT
공식 코드Data Augmentation for Low-Resource Neural Machine Translation
https://github.com/marziehf/variation-generation
공식 코드The Unreasonable Volatility of Neural Machine Translation Models
https://github.com/masakhane-io/afridoc-mt
공식 코드https://github.com/masakhane-io/masakhane-news
공식 코드MasakhaNEWS: News Topic Classification for African languages
https://github.com/masashi-y/depccg
공식 코드A* CCG Parsing with a Supertag and Dependency Factored Model
https://github.com/mastervito/diffusionvg
공식 코드Exploring Iterative Refinement with Diffusion Models for Video Grounding
https://github.com/maszhongming/MatchSum
공식 코드https://github.com/matnlp/hiclre
공식 코드HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction
https://github.com/mattf1n/lm-intervention
공식 코드Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models
https://github.com/matthewcym/gense
공식 코드Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework
https://github.com/matthewcym/sflm
공식 코드Revisiting Self-Training for Few-Shot Learning of Language Model
https://github.com/mattwright99/thesis
공식 코드Design and Implementation of a Quantum Kernel for Natural Language Processing
https://github.com/mauritsbleeker/reducing-predictive-feature-suppression
공식 코드Reducing Predictive Feature Suppression in Resource-Constrained Contrastive Image-Caption Retrieval
https://github.com/maxylee/3am
공식 코드3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset
https://github.com/maybenotime/css
공식 코드A New Dataset and Empirical Study for Sentence Simplification in Chinese
https://github.com/maybenotime/phd
공식 코드A New Benchmark and Reverse Validation Method for Passage-level Hallucination Detection
https://github.com/mayubo2333/paie
공식 코드Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction
https://github.com/mcandito/aux-tasks-biaffine-graph-parser-findingsacl22
공식 코드Auxiliary Tasks to Boost Biaffine Semantic Dependency Parsing
https://github.com/mcgill-nlp/ud-to-meaning
공식 코드https://github.com/mecuping/dlp-lora
공식 코드https://github.com/media-bias-analysis-group/neural-media-bias-detection-using-distant-supervision-with-babe
공식 코드Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Experts
https://github.com/media-bias-group/a-domain-adaptive-pre-training-approach-for-language-biasdetection-in-news
공식 코드A Domain-adaptive Pre-training Approach for Language Bias Detection in News
https://github.com/medialab/twitter-incremental-clustering
공식 코드An Incremental Clustering Baseline for Event Detection on Twitter
https://github.com/meetdavidwan/factpegasus
공식 코드FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization
https://github.com/megagonlabs/autotemplate
공식 코드AutoTemplate: A Simple Recipe for Lexically Constrained Text Generation
https://github.com/megagonlabs/emu
공식 코드Emu: Enhancing Multilingual Sentence Embeddings with Semantic Specialization
https://github.com/megagonlabs/llm-longeval
공식 코드https://github.com/mellody11/less
공식 코드LESS: Label-Efficient and Single-Stage Referring 3D Segmentation
https://github.com/melzohbi/contrastwsd
공식 코드https://github.com/mengcaopku/dcnet
공식 코드Correspondence Matters for Video Referring Expression Comprehension
https://github.com/mengqiaolan/model_enumeration_fo2
공식 코드Model Enumeration of Two-Variable Logic with Quadratic Delay Complexity
https://github.com/mesnico/TERAN
공식 코드Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders
https://github.com/meta-diffub/meta-diffub
공식 코드Meta-DiffuB: A Contextualized Sequence-to-Sequence Text Diffusion Model with Meta-Exploration
https://github.com/mgaido91/FBK-fairseq-ST
공식 코드Contextualized Translation of Automatically Segmented Speech
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.