https://github.com/declare-lab/MELD
공식 코드MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
Papers with Code 아카이브
Dialogue
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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Dialogue is notoriously hard to evaluate. Past approaches have used human evaluation.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/declare-lab/MIME
공식 코드https://github.com/declare-lab/dialogue-understanding
공식 코드https://github.com/declare-lab/exemplary-empathy
공식 코드Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication
https://github.com/declare-lab/segue
공식 코드Sentence Embedder Guided Utterance Encoder (SEGUE) for Spoken Language Understanding
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/dehanalkautsar/indotod
공식 코드IndoToD: A Multi-Domain Indonesian Benchmark For End-to-End Task-Oriented Dialogue Systems
https://github.com/deqiankong/spi
공식 코드Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference
https://github.com/dfki-nlp/interrolang
공식 코드InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations
https://github.com/digitalphonetics/multilingual-seq2seq-slu
공식 코드https://github.com/diligentspring/investigating-the-robustness-of-counterfactual-learning-to-rank-models
공식 코드Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility Study
https://github.com/dingdongwang/mmsu_bench
공식 코드MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark
https://github.com/doctordream/mdia
공식 코드MDIA: A Benchmark for Multilingual Dialogue Generation in 46 Languages
https://github.com/dongguanting/noise-slot-filling-llm
공식 코드https://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/dvlab-research/minigemini
공식 코드Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
https://github.com/dymanne123/PLPCL
공식 코드Pseudo-Label Enhanced Prototypical Contrastive Learning for Uniformed Intent Discovery
https://github.com/eastha0526/beyond-ontology-in-dst
공식 코드Beyond Ontology in Dialogue State Tracking for Goal-Oriented Chatbot
https://github.com/elianap/speechxai
공식 코드Explaining Speech Classification Models via Word-Level Audio Segments and Paralinguistic Features
https://github.com/elnaaz/GCE-Model
공식 코드https://github.com/emanuelevivoli/CoMix
공식 코드CoMix: A Comprehensive Benchmark for Multi-Task Comic Understanding
https://github.com/emora-chat/emora_stdm
공식 코드Emora STDM: A Versatile Framework for Innovative Dialogue System Development
https://github.com/emory-irlab/CDAC
공식 코드Contextual Dialogue Act Classification for Open-Domain Conversational Agents
https://github.com/ernie-research/ma-rlhf
공식 코드MA-RLHF: Reinforcement Learning from Human Feedback with Macro Actions
https://github.com/espnet/espnet
공식 코드ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet
https://github.com/espnet/espnet_onnx
공식 코드https://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/facebookresearch/Zero-Shot-DST
공식 코드Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.