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Found 11 papers, 1 papers with code
IntentDial: An Intent Graph based Multi-Turn Dialogue System with Reasoning Path Visualization
Intent detection and identification from multi-turn dialogue has become a widely explored technique in conversational agents, for example, voice assistants and intelligent customer services.
R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling
Human language understanding operates at multiple levels of granularity (e. g., words, phrases, and sentences) with increasing levels of abstraction that can be hierarchically combined.
Interactive Question Clarification in Dialogue via Reinforcement Learning
In this work, we propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query.
Query Distillation: BERT-based Distillation for Ensemble Ranking
We present a novel two-stage distillation method for ranking problems that allows a smaller student model to be trained while benefitting from the better performance of the teacher model, providing better control of the inference latency and computational burden.
Data Augmentation for Multiclass Utterance Classification -- A Systematic Study
Utterance classification is a key component in many conversational systems.
DAN: Dual-View Representation Learning for Adapting Stance Classifiers to New Domains
We address the issue of having a limited number of annotations for stance classification in a new domain, by adapting out-of-domain classifiers with domain adaptation.
Long Short-Term Sample Distillation
The long-term teacher draws on snapshots from several epochs ago in order to provide steadfast guidance and to guarantee teacher--student differences, while the short-term one yields more up-to-date cues with the goal of enabling higher-quality updates.
Which Channel to Ask My Question? Personalized Customer Service RequestStream Routing using DeepReinforcement Learning
These observations suggest that our proposed method can seek the trade-off where both channel resources and customers' satisfaction are optimal.
Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs
Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA.
