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/aws-samples/aws-lex-noisy-spoken-language-understanding
https://github.com/awslabs/sockeye
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ayeshaenayet/dac-use
A Transfer Learning Approach for Dialogue Act Classification of GitHub Issue Comments
https://github.com/b-etienne/Seq2seq-PyTorch
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/batra-mlp-lab/WAY
https://github.com/batra-mlp-lab/avsd
https://github.com/batra-mlp-lab/visdial
https://github.com/batra-mlp-lab/visdial-challenge-starter-pytorch
https://github.com/batra-mlp-lab/visdial-rl
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
https://github.com/bcaitech1/p3-dst-chatting-day
Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems
https://github.com/bearblog/CoreRank
https://github.com/bentrevett/pytorch-seq2seq
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/bepoetree/mttod
Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context
https://github.com/bkoch4142/attention-is-all-you-need-paper
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/boru-roylu/theta
https://github.com/brainsqueeze/text2vec
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/brightmart/text_classification
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/bruzwen/ddxplus
Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks
https://github.com/cambridgeltl/multi3woz
https://github.com/candoz/memn2n-dialog-babi-keras
https://github.com/cerebroai/AskIt
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/chao-ji/tf-seq2seq
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/chia-hsuan-lee/spoken-squad
https://github.com/chongminggao/cirs-codes
KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
https://github.com/chunghyunhee/twitter_disaster_NLP
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/circlePi/knowledge-driven-dialogue-lic2019
Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue Agent
https://github.com/code-implementation1/Code7/tree/main/SDNet
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/cosmoquester/seq2seq
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/cui0523/Code6/tree/main/duconv
Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue Agent
https://github.com/dalmia/Quora-Question-Pairs
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/danielamassiceti/geneval_visdial
https://github.com/danny911kr/REALTALK
Evaluating Very Long-Term Conversational Memory of LLM Agents
https://github.com/daphne12345/SummarizationRadiologyReports
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/datalogue/keras-attention
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/declare-lab/conv-emotion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.