https://github.com/shuyicao/emo-DA
Dialogue Act Sequence Labeling using Hierarchical encoder with CRF
Papers with Code 아카이브
Dialogue
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 21
- 평가표
- 1
- 보존된 평가 기록
- 1
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/siat-nlp/TTOS
https://github.com/silverriver/PersonalDilaog
https://github.com/silverriver/Stylized_Dialog
Stylized Dialogue Response Generation Using Stylized Unpaired Texts
https://github.com/silviatti/prompt-based-user-simulator
In-Context Learning User Simulators for Task-Oriented Dialog Systems
https://github.com/simonjisu/NMT
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/skoltech-nlp/sgdd-tst
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
https://github.com/slme1109/Lyrics_Generator_Using_LSTM
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/slme1109/lyrics-generator
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/slp-rl/salmon
TASTE: Text-Aligned Speech Tokenization and Embedding for Spoken Language Modeling
https://github.com/smisthzhu/attentionocr
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/sogang-isds/TOATOD
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System
https://github.com/sonalinayak/nlg-eval
https://github.com/songys/Kosentiment
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
https://github.com/songys/KosentimentalTreebank
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
https://github.com/sonos/nlu-benchmark
https://github.com/sonos/spoken-language-understanding-research-datasets
https://github.com/sooftware/Attention-Implementation
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/sooftware/attentions
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/sooftware/nlp-attentions
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/stanford-oval/genienlp
Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues
https://github.com/stanford-oval/schema2qa
A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representation
https://github.com/sujoung/debuggedDDQ
Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning
https://github.com/sunbopds/SDEN-Pytorch-master
Sequential Dialogue Context Modeling for Spoken Language Understanding
https://github.com/sunnysinghnitb/text-corrector-software
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/sunnysinghnitb/text_corrector_software
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/sunzeyeah/rlhf
https://github.com/suryachintu/Quora-Insincere-Questions-Kaggle
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/swe-zzf/esim-response-selection
Sequential Attention-based Network for Noetic End-to-End Response Selection
https://github.com/switchsyj/adae2ml-xsf
https://github.com/sxjscience/GluonNLP-Slot-Filling
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents
https://github.com/tanayz/TC-Bot-py3
https://github.com/tanyuqian/ctc-gen-eval
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
https://github.com/tfolkman/deep-learning-experiments
https://github.com/the-pythoncoder/councelChatbot2
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/the-pythoncoder/counsel-chat
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.