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Found 13 papers, 5 papers with code
Joint Reasoning on Hybrid-knowledge sources for Task-Oriented Dialog
Traditional systems designed for task oriented dialog utilize knowledge present only in structured knowledge sources to generate responses.
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End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs
We propose a novel problem within end-to-end learning of task-oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e. g., car not starting).
Constraint based Knowledge Base Distillation in End-to-End Task Oriented Dialogs
In this paper, we propose a novel filtering technique that consists of (1) a pairwise similarity based filter that identifies relevant information by respecting the n-ary structure in a KB record.
Unsupervised Learning of KB Queries in Task-Oriented Dialogs
Task-oriented dialog (TOD) systems often need to formulate knowledge base (KB) queries corresponding to the user intent and use the query results to generate system responses.
Mask & Focus: Conversation Modelling by Learning Concepts
The proposed model, referred to as Mask \& Focus maps the input context to a sequence of concepts which are then used to generate the response concepts.
Unsupervised Learning of Interpretable Dialog Models
However these states need to be handcrafted and annotated in the data.
Multi-level Memory for Task Oriented Dialogs
Instead of using triples to store KB results, we introduce a novel multi-level memory architecture consisting of cells for each query and their corresponding results.
Disentangling Language and Knowledge in Task-Oriented Dialogs
We also systematically modify existing datasets to measure disentanglement and show BoSsNet to be robust to KB modifications.
Generating Natural Language Question-Answer Pairs from a Knowledge Graph Using a RNN Based Question Generation Model
To generate such QA pairs, we first extract a set of keywords from entities and relationships expressed in a triple stored in the knowledge graph.
