SOTAVerified

Dialogue Generation

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).

Papers

Showing 476500 of 606 papers

TitleStatusHype
Response Generation with Context-Aware Prompt Learning0
Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Survey0
X-ReCoSa: Multi-Scale Context Aggregation For Multi-Turn Dialogue Generation0
Rethinking the Agreement in Human Evaluation Tasks0
Retrieval-based Goal-Oriented Dialogue Generation0
RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback0
RMHIDD: A Reddit Mental Health Intervention Dialogue Dataset0
Robust Task-Oriented Dialogue Generation with Contrastive Pre-training and Adversarial Filtering0
Knowledge Injection into Dialogue Generation via Language Models0
A Stack-Propagation Framework for Low-Resource Personalized Dialogue Generation0
A Speaker-aware Parallel Hierarchical Attentive Encoder-Decoder Model for Multi-turn Dialogue Generation0
SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues0
A Shoulder to Cry on: Towards A Motivational Virtual Assistant for Assuaging Mental Agony0
Scene-Aware Prompt for Multi-modal Dialogue Understanding and Generation0
A Semi-Supervised Stable Variational Network for Promoting Replier-Consistency in Dialogue Generation0
A Benchmark for Understanding and Generating Dialogue between Characters in Stories0
UPCS: Unbiased Persona Construction for Dialogue Generation0
Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References0
Self-Attentional Models Application in Task-Oriented Dialogue Generation Systems0
Self-Attention-Based Message-Relevant Response Generation for Neural Conversation Model0
Adaptive Bridge between Training and Inference for Dialogue Generation0
Semantic Content Prediction for Generating Interviewing Dialogues to Elicit Users’ Food Preferences0
Semantic Diversity in Dialogue with Natural Language Inference0
Semantic Guidance of Dialogue Generation with Reinforcement Learning0
User-Specific Dialogue Generation with User Profile-Aware Pre-Training Model and Parameter-Efficient Fine-Tuning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LMEDRAvg F121.99Unverified
2P^2 BotAvg F119.77Unverified
3TransferTransfoAvg F119.09Unverified
4Seq2Seq + AttentionAvg F116.18Unverified
5Synthesizer (R+V)BLEU-114.7Unverified
6KV Profile MemoryAvg F111.9Unverified
#ModelMetricClaimedVerifiedStatus
1Classification-based modelSlot Accuracy0.97Unverified
2Two-in-one modelSlot Accuracy0.97Unverified
#ModelMetricClaimedVerifiedStatus
1EVAmauve0.97Unverified
2Per-BOBmauve0.95Unverified
#ModelMetricClaimedVerifiedStatus
1mm1 in 10 R@25Unverified
#ModelMetricClaimedVerifiedStatus
1∞-former (Sticky memories)F19.01Unverified
#ModelMetricClaimedVerifiedStatus
1∞-former (Sticky memories + initialized GPT-2 Small)Perplexity32.48Unverified
#ModelMetricClaimedVerifiedStatus
1SpaceFusioninterest (human)2.53Unverified
#ModelMetricClaimedVerifiedStatus
1MrRNN Act.-Ent.F14.63Unverified
#ModelMetricClaimedVerifiedStatus
1MrRNN Act.-Ent.Accuracy34.48Unverified
#ModelMetricClaimedVerifiedStatus
1MrRNN Act.-Ent.F111.43Unverified
#ModelMetricClaimedVerifiedStatus
1MrRNN Act.-Ent.Accuracy95.04Unverified
#ModelMetricClaimedVerifiedStatus
1MrRNN Act.-Ent.F13.72Unverified
#ModelMetricClaimedVerifiedStatus
1MrRNN Act.-Ent.Accuracy29.01Unverified