Dialogue Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 13
- 보존된 평가 기록
- 20
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/valdersoul/bn-vae
공식 코드A Batch Normalized Inference Network Keeps the KL Vanishing Away
https://github.com/vityavitalich/imad
공식 코드https://github.com/vivian556123/neurips2024-covomix
공식 코드CoVoMix: Advancing Zero-Shot Speech Generation for Human-like Multi-talker Conversations
https://github.com/wengsyx/lingyi
공식 코드LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs
https://github.com/wjko2/Domain-Agnostic-Sentence-Specificity-Prediction
공식 코드https://github.com/wlr737/emnlp2022-seek
공식 코드Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection
https://github.com/wlr737/sibyl
공식 코드https://github.com/wyu-du/controlled-dialogue-generation
공식 코드SideControl: Controlled Open-domain Dialogue Generation via Additive Side Networks
https://github.com/wyu-du/self-training-dialogue-generation
공식 코드Self-training with Two-phase Self-augmentation for Few-shot Dialogue Generation
https://github.com/xmshi-trio/MidMed
공식 코드MidMed: Towards Mixed-Type Dialogues for Medical Consultation
https://github.com/xu1998hz/sescore2
공식 코드SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes
https://github.com/yanguojun123/medical-dialogue
공식 코드ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues
https://github.com/yezato11/INTERSPEECH_Media
공식 코드https://github.com/yizhen20133868/Retriever-Dialogue
공식 코드Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever
https://github.com/yongcaoplus/cudialog
공식 코드Bridging Cultural Nuances in Dialogue Agents through Cultural Value Surveys
https://github.com/yul091/dgslow
공식 코드White-Box Multi-Objective Adversarial Attack on Dialogue Generation
https://github.com/yuxiaoleemarks/cma
공식 코드Concept Matching with Agent for Out-of-Distribution Detection
https://github.com/zeng-wh/seen-to-unseen
공식 코드https://github.com/zequnl/CMAML
공식 코드Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks
https://github.com/zgahhblhc/Debiased-Chat
공식 코드Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning
https://github.com/zhangbo-nlp/kedit
공식 코드Efficient Tuning of Large Language Models for Knowledge-Grounded Dialogue Generation
https://github.com/zhangbo-nlp/vikdf
공식 코드https://github.com/zhangbo-nlp/zrigf
공식 코드ZRIGF: An Innovative Multimodal Framework for Zero-Resource Image-Grounded Dialogue Generation
https://github.com/zhanghainan/ReCoSa
공식 코드ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation
https://github.com/zhaoxlpku/KnowledGPT
공식 코드Knowledge-Grounded Dialogue Generation with Pre-trained Language Models
https://github.com/zhouzhouyang520/empatheticdialoguegeneration_escm
공식 코드Exploiting Emotion-Semantic Correlations for Empathetic Response Generation
https://github.com/zhuyunqi96/LoraLPrun
공식 코드Parameter-Efficient Fine-Tuning with Layer Pruning on Free-Text Sequence-to-Sequence Modeling
https://github.com/zyang-ur/onestage_grounding
공식 코드Multimodal Incremental Transformer with Visual Grounding for Visual Dialogue Generation
https://github.com/10-zin/Synthesizer
Synthesizer: Rethinking Self-Attention in Transformer Models
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Pangu_alpha
https://github.com/2023-MindSpore-1/ms-code-162
https://github.com/2023-MindSpore-1/ms-code-200
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/2023-MindSpore-1/ms-code-219/tree/main/duconv
Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue Agent
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/pangualpha
PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language Model
https://github.com/A-Jacobson/minimal-nmt
Neural Machine Translation by Jointly Learning to Align and Translate
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.