Papers with Code 아카이브
Conversational Response Selection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 15
- 보존된 평가 기록
- 90
Conversational response selection refers to the task of identifying the most relevant response to a given input sentence from a collection of sentences.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems
Learning Dialogue Representations from Consecutive Utterances
One Agent To Rule Them All: Towards Multi-agent Conversational AI
Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue
Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning
Exploring Dense Retrieval for Dialogue Response Selection
MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding
Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems
Open-domain question classification and completion in conversational information search
Dialogue Response Selection with Hierarchical Curriculum Learning
Dialogue Response Ranking Training with Large-Scale Human Feedback Data
Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues
Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection
Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection
Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
ConveRT: Efficient and Accurate Conversational Representations from Transformers
TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-based Chatbots
Multi-Granularity Representations of Dialog
An Effective Domain Adaptive Post-Training Method for BERT in Response Selection
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring
A Repository of Conversational Datasets
Sequential Attention-based Network for Noetic End-to-End Response Selection
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
Building Sequential Inference Models for End-to-End Response Selection
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Modeling Multi-turn Conversation with Deep Utterance Aggregation
Universal Sentence Encoder
Deep contextualized word representations
Personalizing Dialogue Agents: I have a dog, do you have pets too?
Addressee and Response Selection in Multi-Party Conversations with Speaker Interaction RNNs
Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots
Improved Deep Learning Baselines for Ubuntu Corpus Dialogs
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.