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/tongbao96/code-for-sfr-as
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
https://github.com/tree-park/kor-to-eng-translation
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/uber-research/PPLM
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
https://github.com/umeiko/mindspore-seq2seq
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/umutguneri/Question-Answering-Assistant
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/uzi0espil/research-papers-implementation/tree/master/Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/vGkatsis/Chat_Bot_DL
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/varun-bhaseen/Image-caption-generation-using-attention-model
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/vikua/keras-attention-models
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/wangmz15/Chinese-Error-Correction-with-THUMT
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/wayalhruhi/julianser
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
https://github.com/wyu97/permgen
Text Generation from Knowledge Graphs with Graph Transformers
https://github.com/xhlulu/arxiv-assistant
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/xingniu/sockeye
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/xnancy/convai-transformer
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/yinghao1019/NLP_and_DL_practice
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ykrmm/ICLR_2020
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ykrmm/TREMBA
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/yurayli/stanford-cs224n-sol
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/zengyan-97/MultiT-C-Dialog
A Simple and Efficient Multi-Task Learning Approach for Conditioned Dialogue Generation
https://github.com/zhang0jhon/AttentionOCR
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/zhanghainan/RNN-S2S
https://github.com/zhanghainan/RNN-encdec
https://github.com/zhanghainan/ReinforcingCoherence
https://github.com/zhanghainan/TailoredSeq2Seq2DifferentConversationScenarios
https://github.com/zhanghainan/s2s-with-attention
https://github.com/zpschang/seqGAN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.