https://github.com/armelrandy/icl-mt
공식 코드In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation
Papers with Code 아카이브
Sentence Embeddings
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/asparius/unsee
공식 코드https://github.com/atr-dbi/scanqa
공식 코드ScanQA: 3D Question Answering for Spatial Scene Understanding
https://github.com/ayyyq/tara
공식 코드An AMR-based Link Prediction Approach for Document-level Event Argument Extraction
https://github.com/ba1jun/model-selection-airtran
공식 코드Leveraging Estimated Transferability Over Human Intuition for Model Selection in Text Ranking
https://github.com/babelscape/amr-alignment
공식 코드Cross-lingual AMR Aligner: Paying Attention to Cross-Attention
https://github.com/bcmi220/esc4nmt
공식 코드Explicit Sentence Compression for Neural Machine Translation
https://github.com/bdbc-kg-nlp/ngcse
공식 코드https://github.com/bernard-yang/simamr
공식 코드https://github.com/bic4907/insturcted_pcgrl
공식 코드IPCGRL: Language-Instructed Reinforcement Learning for Procedural Level Generation
https://github.com/bin199/informin-cl
공식 코드https://github.com/binwang28/evalrank-embedding-evaluation
공식 코드Just Rank: Rethinking Evaluation with Word and Sentence Similarities
https://github.com/binwang28/rse
공식 코드Relational Sentence Embedding for Flexible Semantic Matching
https://github.com/bohanli/BERT-flow
공식 코드https://github.com/brmson/dataset-sts
공식 코드Joint Learning of Sentence Embeddings for Relevance and Entailment
https://github.com/caskcsg/sentemb
공식 코드InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings
https://github.com/caskcsg/sentemb/tree/main/ESimCSE
공식 코드ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding
https://github.com/caskcsg/sentemb/tree/main/InfoCSE
공식 코드InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings
https://github.com/caskcsg/sentemb/tree/main/gsInfoNCE
공식 코드Smoothed Contrastive Learning for Unsupervised Sentence Embedding
https://github.com/causalnlp/amr_llm
공식 코드Analyzing the Role of Semantic Representations in the Era of Large Language Models
https://github.com/cbaziotis/seq3
공식 코드https://github.com/ccilab-doshisha/sdjc
공식 코드https://github.com/ccliu2/syn-emb
공식 코드https://github.com/chakib401/smoothing_sentence_embeddings
공식 코드More Discriminative Sentence Embeddings via Semantic Graph Smoothing
https://github.com/chanind/amr-logic-converter
공식 코드https://github.com/chanind/amr-social-chemistry-reasoner
공식 코드https://github.com/chanind/tensor-theorem-prover
공식 코드https://github.com/chenllliang/atp
공식 코드ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs
https://github.com/chillingdream/dap
공식 코드Dual-Alignment Pre-training for Cross-lingual Sentence Embedding
https://github.com/christos42/CLDR_CLNER_models
공식 코드Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning
https://github.com/circle-ming/multicsr
공식 코드https://github.com/clcl-geneva/blm-snfdisentangling
공식 코드Grammatical information in BERT sentence embeddings as two-dimensional arrays
https://github.com/coisini-glenda/wsdan-for-medical-visual-question-answering
공식 코드https://github.com/complementizer/rl-sentence-compression
공식 코드Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning
https://github.com/cosmmb/DCNN
공식 코드Dependency-based Convolutional Neural Networks for Sentence Embedding
https://github.com/cuiniki/llms-are-not-scorers
공식 코드LLMs Are Not Scorers: Rethinking MT Evaluation with Generation-Based Methods
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.