https://github.com/mangonihao/GLMP_Annotation
Global-to-local Memory Pointer Networks for Task-Oriented Dialogue
Papers with Code 아카이브
Dialogue
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 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/mangopy/direct-rag-learning
Wizard of Wikipedia: Knowledge-Powered Conversational agents
https://github.com/markWJJ/TC-Bot
https://github.com/maszhongming/unieval
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
https://github.com/mayurnewase/Translation
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/mcgill-nlp/faithdial
FaithDial: A Faithful Benchmark for Information-Seeking Dialogue
https://github.com/meera1hahn/Graph_LED
https://github.com/megagonlabs/llm-longeval
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
https://github.com/mengyuest/reactive_cbf
Reactive and Safe Road User Simulations using Neural Barrier Certificates
https://github.com/microsoft/bang
BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining
https://github.com/mike-a-yen/date-translation
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/mila-iqia/ddxplus
Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks
https://github.com/milmor/GPT
https://github.com/mindspore-ai/models/blob/master/research/nlp/gpt2
https://github.com/mindspore-ai/models/tree/master/official/nlp/pangu_alpha
https://github.com/mindspore-courses/DeepNLP-models-MindSpore
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/minimaxir/gpt-2-simple
https://github.com/miohana/vqa
Hierarchical Question-Image Co-Attention for Visual Question Answering
https://github.com/misonsky/multilingualdatasets
MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation
https://github.com/mlnlp-world/top-ai-conferences-paper-with-code
https://github.com/modelscope/FunCodec
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT
https://github.com/moon23k/Attention_Anchors
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/mp2893/gram
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/mpandeydev/SDnetmod
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/mpavlovic/insincere-questions-classifier
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/neqkir/attention-mechanism
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/nid989/teach_edh
https://github.com/nlpdata/dream
DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension
https://github.com/nonslowrunner/Visualdialogs
https://github.com/noriyukipy/gptchat
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/nrc-cnrc/sockeye-multisource
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/nvshrao/AlignAndTranslate
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/nyu-dl/dl4dial-bayesian-calibration
Importance of Search and Evaluation Strategies in Neural Dialogue Modeling
https://github.com/openai/gpt-2
https://github.com/oswaldoludwig/Parallel-Seq2Seq
End-to-end Adversarial Learning for Generative Conversational Agents
https://github.com/para-zhou/RL_DDQ
Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.