Dialogue Generation
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Dialogue generation is the task of "understanding" natural language inputs - within natural language processing in order to produce output. The systems are usually intended for conversing with humans, for instance back and forth dialogue with a conversation agent like a chatbot. Some example benchmarks for this task (see others such as Natural Language Understanding) include FusedChat and Ubuntu DIalogue Corpus (UDC). Models can be evaluated via metrics such as BLEU, ROUGE, and METEOR albeit with challenges in terms of weak correlation with human judgement, that may be addressed by new ones like UnSupervised and Reference-free (USR) and Metric for automatic Unreferenced dialog evaluation (MaUde).
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/chenrj233/lmedr
공식 코드Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues
https://github.com/chenxu05037/COSPLAY
공식 코드COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas
https://github.com/chin-gyou/Div-Non-Conv
공식 코드Diversifying Dialogue Generation with Non-Conversational Text
https://github.com/cl-tohoku/eval-via-selection
공식 코드Evaluating Dialogue Generation Systems via Response Selection
https://github.com/cods-gcs/chatty-gen
공식 코드Dialogue Benchmark Generation from Knowledge Graphs with Cost-Effective Retrieval-Augmented LLMs
https://github.com/csebuetnlp/banglanlg
공식 코드https://github.com/declare-lab/MELD
공식 코드MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
https://github.com/deekshavarshney/cdialog
공식 코드CDialog: A Multi-turn Covid-19 Conversation Dataset for Entity-Aware Dialog Generation
https://github.com/deekshavarshney/cntf
공식 코드Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation
https://github.com/deep-spin/infinite-former
공식 코드https://github.com/deep-spin/sparse_text_generation
공식 코드https://github.com/deqiankong/spi
공식 코드Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference
https://github.com/doctordream/mdia
공식 코드MDIA: A Benchmark for Multilingual Dialogue Generation in 46 Languages
https://github.com/dreasysnail/RetGen
공식 코드RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling
https://github.com/drivetosouth/safedialbench-dataset
공식 코드https://github.com/emanuelevivoli/CoMix
공식 코드CoMix: A Comprehensive Benchmark for Multi-Task Comic Understanding
https://github.com/ernie-research/ma-rlhf
공식 코드MA-RLHF: Reinforcement Learning from Human Feedback with Macro Actions
https://github.com/ewsheng/controllable-nlg-biases
공식 코드https://github.com/ewsheng/nlg-bias
공식 코드https://github.com/facebook/MemNN
공식 코드https://github.com/facebookresearch/EmpatheticDialogues
공식 코드Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
https://github.com/facebookresearch/ParlAI
공식 코드Personalizing Dialogue Agents: I have a dog, do you have pets too?
https://github.com/fantast4ever/knowprefix-tuning
공식 코드KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for Knowledge-Grounded Dialogue Generation
https://github.com/geri-emp/CAB
공식 코드CAB: Empathetic Dialogue Generation with Cognition, Affection and Behavior
https://github.com/ghosthamlet/persona
공식 코드Improvement of a dedicated model for open domain persona-aware dialogue generation
https://github.com/guopeiming/E2E_PSDG
공식 코드An End-to-End Model for Photo-Sharing Multi-modal Dialogue Generation
https://github.com/ha-lins/GEML-MDG
공식 코드Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation
https://github.com/hannibal046/selfmemory
공식 코드Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory
https://github.com/hanscal/dcks
공식 코드Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge
https://github.com/hengyicai/AdaND
공식 코드https://github.com/hengyicai/Adaptive_Multi-curricula_Learning_for_Dialog
공식 코드Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation
https://github.com/hengyicai/ContrastiveLearning4Dialogue
공식 코드Group-wise Contrastive Learning for Neural Dialogue Generation
https://github.com/hmc-cs-mdrissi/fairseq
공식 코드https://github.com/holi-lab/tooldial
공식 코드ToolDial: Multi-turn Dialogue Generation Method for Tool-Augmented Language Models
https://github.com/hongyuanluke/pcc
공식 코드PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.