https://github.com/anupsingh15/BEST-STD
공식 코드BEST-STD: Bidirectional Mamba-Enhanced Speech Tokenization for Spoken Term Detection
Papers with Code 아카이브
Dialogue
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/apple/dmel
공식 코드https://github.com/apple/ml-cread
공식 코드CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues
https://github.com/apple/ml-sage-dialog-gen
공식 코드SAGE: Steering and Refining Dialog Generation with State-Action Augmentation
https://github.com/arjunakula/Visual-Discourse-Parsing
공식 코드https://github.com/asappresearch/abcd
공식 코드https://github.com/asappresearch/constrained-dialogue-generation
공식 코드Long-term Control for Dialogue Generation: Methods and Evaluation
https://github.com/asappresearch/slue-toolkit
공식 코드SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech
https://github.com/asappresearch/spoken-ner
공식 코드On the Use of External Data for Spoken Named Entity Recognition
https://github.com/ashi-ta/speechglue
공식 코드SpeechGLUE: How Well Can Self-Supervised Speech Models Capture Linguistic Knowledge?
https://github.com/aub-mind/Arabic-Empathetic-Chatbot
공식 코드Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little Data
https://github.com/awslabs/pptod
공식 코드Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System
https://github.com/ayeshaEnayet/Dialogue-Act-for-dialogue-analysis-
공식 코드A Transfer Learning Approach for Dialogue Act Classification of GitHub Issue Comments
https://github.com/babelscape/alert
공식 코드ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming
https://github.com/bashlab/quads
공식 코드QUADS: QUAntized Distillation Framework for Efficient Speech Language Understanding
https://github.com/bat-reality/ddrcu
공식 코드Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation
https://github.com/batra-mlp-lab/visdial-amt-chat
공식 코드https://github.com/behavioral-data/PARTNER
공식 코드https://github.com/believewhat/dr.noteaid
공식 코드NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes
https://github.com/bernard-yang/meddiaamr
공식 코드Improving Medical Dialogue Generation with Abstract Meaning Representations
https://github.com/bhigy/textual-supervision
공식 코드Textual Supervision for Visually Grounded Spoken Language Understanding
https://github.com/bkane2/habitual-response-generation
공식 코드We are what we repeatedly do: Inducing and deploying habitual schemas in persona-based responses
https://github.com/blmoistawinde/dasc
공식 코드Semantic Space Grounded Weighted Decoding for Multi-Attribute Controllable Dialogue Generation
https://github.com/boru-roylu/encode-once-and-decode-in-parallel
공식 코드Efficient Encoder-Decoder Transformer Decoding for Decomposable Tasks
https://github.com/bosheng2020/globalwoz
공식 코드GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems
https://github.com/bothe/EDAs
공식 코드EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators
https://github.com/bothe/dialogue-act-recognition
공식 코드https://github.com/briansidp/espnet
공식 코드End-to-end Spoken Language Understanding with Tree-constrained Pointer Generator
https://github.com/budzianowski/multiwoz
공식 코드https://github.com/butterfliesss/EmpRL
공식 코드Empathy Level Alignment via Reinforcement Learning for Empathetic Response Generation
https://github.com/cambridgeltl/e2e_tod_toolkit
공식 코드https://github.com/cambridgeltl/post-specialisation
공식 코드Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources
https://github.com/cambridgeltl/response_reranking
공식 코드Reranking Overgenerated Responses for End-to-End Task-Oriented Dialogue Systems
https://github.com/caohy123/EKTC
공식 코드TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLM
https://github.com/caoyu-noob/Multi-GPT2
공식 코드Pretrained Language Models for Dialogue Generation with Multiple Input Sources
https://github.com/caoyu-noob/d3
공식 코드A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.