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Open-Domain Dialog
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/johndmendonca/medal
공식 코드https://github.com/johndmendonca/soda_eval
공식 코드Soda-Eval: Open-Domain Dialogue Evaluation in the age of LLMs
https://github.com/jvasselli/dialogue-metric-robustness
공식 코드Measuring the Robustness of Reference-Free Dialogue Evaluation Systems
https://github.com/kwai/dialogbench
공식 코드https://github.com/lfdharo/DSTC10_Track5_Toxicity
공식 코드Automatic Evaluation and Moderation of Open-domain Dialogue Systems
https://github.com/li3cmz/GRADE
공식 코드GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems
https://github.com/martiansideofthemoon/hurdles-longform-qa
공식 코드https://github.com/maximedb/full
공식 코드https://github.com/microsoft/ProphetNet
공식 코드https://github.com/microsoft/godel
공식 코드https://github.com/mike-n-7/ADEM
공식 코드Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses
https://github.com/natashamjaques/neural_chat
공식 코드Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems
https://github.com/nouhadziri/DialogEntailment
공식 코드https://github.com/nouhadziri/THRED
공식 코드Augmenting Neural Response Generation with Context-Aware Topical Attention
https://github.com/pluslabnlp/deam
공식 코드DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations
https://github.com/prakharguptaz/Adv_gen_dialogue
공식 코드Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation
https://github.com/prakharguptaz/Instructdial
공식 코드InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning
https://github.com/prakharguptaz/multirefeval
공식 코드Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple References
https://github.com/renll/c-pmi
공식 코드C-PMI: Conditional Pointwise Mutual Information for Turn-level Dialogue Evaluation
https://github.com/royny/self-eval
공식 코드https://github.com/ruckbreasoning/glm-dialog
공식 코드GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation
https://github.com/sashank06/INLG_eval
공식 코드Towards Best Experiment Design for Evaluating Dialogue System Output
https://github.com/scutcyr/CPED
공식 코드CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI
https://github.com/sfillwo/dialogueeval-annotatorimpact
공식 코드Exploring the Impact of Human Evaluator Group on Chat-Oriented Dialogue Evaluation
https://github.com/shikib/fed
공식 코드https://github.com/shikib/generative_slot_filling
공식 코드GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling
https://github.com/shikib/usr
공식 코드USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation
https://github.com/sopilnyak/headline-selection
공식 코드Transformers for Headline Selection for Russian News Clusters
https://github.com/suvodipdey/bok
공식 코드BoK: Introducing Bag-of-Keywords Loss for Interpretable Dialogue Response Generation
https://github.com/tianboji/dialogue-eval
공식 코드Achieving Reliable Human Assessment of Open-Domain Dialogue Systems
https://github.com/ukplab/dior-cvae
공식 코드Dior-CVAE: Pre-trained Language Models and Diffusion Priors for Variational Dialog Generation
https://github.com/vgtomahawk/dialog-fig-speech-robust
공식 코드Investigating Robustness of Dialog Models to Popular Figurative Language Constructs
https://github.com/vitouphy/usl_dialogue_metric
공식 코드Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systems
https://github.com/yhlleo/frechet-bert-distance
공식 코드https://github.com/yubol-cmu/awesome-multi-turn-llms
공식 코드Beyond Single-Turn: A Survey on Multi-Turn Interactions with Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.