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/jonas-scholz123/msci-project
Dialogue Act Sequence Labeling using Hierarchical encoder with CRF
https://github.com/jonathanmli/avalon-llm
Strategist: Learning Strategic Skills by LLMs via Bi-Level Tree Search
https://github.com/jsbaan/DPAC-DialogueGAN
https://github.com/jsnouffer/transfer-learning-conv-ai
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/julianser/hed-dlg-truncated
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
https://github.com/justinchiu/NeuralDialog
https://github.com/karunraju/VQA
Hierarchical Question-Image Co-Attention for Visual Question Answering
https://github.com/kdexd/lang-emerge-parlai
https://github.com/keyeun/adaptive-vp
https://github.com/kingsaint/Wikidata-Descriptions
https://github.com/kolk/MODELING-ASR-AMBIGUITY-FOR-NEURAL-DIALOGUE-STATE-TRACKING-USING-WORD-CONFUSION-NETWORKS
https://github.com/la-serene/English-German-Translation-System
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/labdac/charlacompling
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/laserene/English-German-Translation-System
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ldulcic/chatbot
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/lefteris12/multitask-schema-guided-dst
A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking
https://github.com/leishu02/EMNLP2019_gCAS
https://github.com/lemuria-wchen/DialogVED
Personalizing Dialogue Agents: I have a dog, do you have pets too?
https://github.com/leob03/Image_captionning
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/leondiao0427/seas
ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming
https://github.com/levis0045/snips-nlu
https://github.com/lexmen318/metaCAT
https://github.com/liliaellouz/wabi-sabi
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/liuyuemaicha/Adversarial-Learning-for-Neural-Dialogue-Generation-in-Tensorflow
https://github.com/liuyuemaicha/Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow
https://github.com/lizi-git/MMConv
https://github.com/lkfo415579/MT-Readling-List
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/lonePatient/knowledge-driven-dialogue-lic2019-rank5
Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue Agent
https://github.com/loremdai/Duelling_D3Q
Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning
https://github.com/lorenlugosch/pretrain_speech_model
Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models
https://github.com/lrank/Linguistic_adversity
https://github.com/lucylow/En_francais_si_vous_plait-
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/lvyufeng/bert4ms
https://github.com/macabdul9/CASA-Dialogue-Act-Classifier
Dialogue Act Classification with Context-Aware Self-Attention
https://github.com/maikezuefle/contr-pretraining
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.