Goal-Oriented Dialog
24 papers with code • 1 benchmarks • 6 datasets
Achieving a pre-defined goal through a dialog.
Doc2Dial
DSTC7 Task 1
Latest papers with no code
Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic
Dialog Structure Induction (DSI) is the task of inferring the latent dialog structure (i. e., a set of dialog states and their temporal transitions) of a given goal-oriented dialog.
Generalized zero-shot audio-to-intent classification
Our multimodal training approach improves the accuracy of zero-shot intent classification on unseen intents of SLURP by 2. 75% and 18. 2% for the SLURP and internal goal-oriented dialog datasets, respectively, compared to audio-only training.
CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation
Practical dialog systems need to deal with various knowledge sources, noisy user expressions, and the shortage of annotated data.
Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems
The rapidly growing market demand for automatic dialogue agents capable of goal-oriented behavior has caused many tech-industry leaders to invest considerable efforts into task-oriented dialog systems.
Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks
For each goal-oriented dialog task of interest, large amounts of data need to be collected for end-to-end learning of a neural dialog system.
Joint System-Wise Optimization for Pipeline Goal-Oriented Dialog System
Recent work (Takanobu et al., 2020) proposed the system-wise evaluation on dialog systems and found that improvement on individual components (e. g., NLU, policy) in prior work may not necessarily bring benefit to pipeline systems in system-wise evaluation.
Domain Expert Platform for Goal-Oriented Dialog Collection
Today, most dialogue systems are fully or partly built using neural network architectures.
Dialog Simulation with Realistic Variations for Training Goal-Oriented Conversational Systems
Our approach includes a novel goal-sampling technique for sampling plausible user goals and a dialog simulation technique that uses heuristic interplay between the user and the system (Alexa), where the user tries to achieve the sampled goal.
Turn-level Dialog Evaluation with Dialog-level Weak Signals for Bot-Human Hybrid Customer Service Systems
We developed a machine learning approach that quantifies multiple aspects of the success or values in Customer Service contacts, at anytime during the interaction.
Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment
Existing end-to-end dialog systems perform less effectively when data is scarce.
