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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/satzbeleg/study-hrp
공식 코드https://github.com/sayantan11995/event-timeline-generation-from-documents
공식 코드Placing (Historical) Facts on a Timeline: A Classification cum Coref Resolution Approach
https://github.com/sayarghoshroy/infopopularity
공식 코드https://github.com/schen149/sub-sentence-encoder
공식 코드Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations
https://github.com/schlevik/sam
공식 코드Semantics Altering Modifications for Evaluating Comprehension in Machine Reading
https://github.com/schmiflo/crf-generation
공식 코드https://github.com/sciencenlp-lab/dimb-re
공식 코드DiMB-RE: Mining the Scientific Literature for Diet-Microbiome Associations
https://github.com/sciencenlp-lab/llm-ssc
공식 코드Multi-label Sequential Sentence Classification via Large Language Model
https://github.com/scsmuhio/MTGCN_Resources
공식 코드https://github.com/sczwangxiao/tsgvs-mm2023
공식 코드https://github.com/sdadas/polish-sentence-evaluation
공식 코드https://github.com/seagate/softsensingtransformer
공식 코드Soft Sensing Transformer: Hundreds of Sensors are Worth a Single Word
https://github.com/seanbenhur/multilingual_aggresive_gender_bias_communal_bias_identifcation
공식 코드Hypers at ComMA@ICON: Modelling Aggressiveness, Gender Bias and Communal Bias Identification
https://github.com/seantrott/raw-c
공식 코드RAW-C: Relatedness of Ambiguous Words--in Context (A New Lexical Resource for English)
https://github.com/sebajoe/multicochrane
공식 코드https://github.com/sebastianGehrmann/diverse_ensembling
공식 코드End-to-End Content and Plan Selection for Data-to-Text Generation
https://github.com/sebastianarnold/cdv
공식 코드Learning Contextualized Document Representations for Healthcare Answer Retrieval
https://github.com/sebischair/fusionsent
공식 코드Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many Classes
https://github.com/sebschu/discourse-entity-lm
공식 코드https://github.com/sedrickkeh/pineapple
공식 코드https://github.com/seekingdream/nmtsloth
공식 코드LLMEffiChecker: Understanding and Testing Efficiency Degradation of Large Language Models
https://github.com/segment-any-text/wtpsplit
공식 코드Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation
https://github.com/selenashe/scone
공식 코드ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning
https://github.com/selimfirat/bilkent-turkish-writings-dataset
공식 코드https://github.com/semantic-textual-relatedness/semantic_relatedness_semeval2024
공식 코드SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages
https://github.com/sense-gvt/sncse
공식 코드SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples
https://github.com/sent-subsent-embs/clustering-network-analysis
공식 코드Clustering and Network Analysis for the Embedding Spaces of Sentences and Sub-Sentences
https://github.com/seonghoon-yu/zero-shot-ris
공식 코드Zero-shot Referring Image Segmentation with Global-Local Context Features
https://github.com/seongminp/graph-dialogue-summary
공식 코드Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion
https://github.com/seongminp/hyperseg
공식 코드Unsupervised Extractive Dialogue Summarization in Hyperdimensional Space
https://github.com/seq-to-mind/semi-style-transfer
공식 코드https://github.com/serenayj/ABCD-ACL2021
공식 코드ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences
https://github.com/serenayj/DeSSE
공식 코드ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences
https://github.com/seungheondoh/msd-subsets
공식 코드https://github.com/seungheondoh/msu-benchmark
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.