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Found 12 papers, 3 papers with code
Automated Question Generation on Tabular Data for Conversational Data Exploration
Few other works provide a view of some interesting slices of data but it is still difficult for the user to draw relevant insights from them.
Towards Automated Evaluation of Explanations in Graph Neural Networks
Explaining Graph Neural Networks predictions to end users of AI applications in easily understandable terms remains an unsolved problem.
Development of an Enterprise-Grade Contract Understanding System
Contracts are arguably the most important type of business documents.
VeeAlign: Multifaceted Context Representation using Dual Attention for Ontology Alignment
Ontology Alignment is an important research problem applied to various fields such as data integration, data transfer, data preparation, etc.
Multifaceted Context Representation using Dual Attention for Ontology Alignment
Ontology Alignment is an important research problem that finds application in various fields such as data integration, data transfer, data preparation etc.
A Practical Dialogue-Act-Driven Conversation Model for Multi-Turn Response Selection
This paper proposes an end-to-end multi-task model for conversation modeling, which is optimized for two tasks, dialogue act prediction and response selection, with the latter being the task of interest.
Compliance Change Tracking in Business Process Services
Compliance officers responsible for maintaining adherence constantly struggle to keep up with the large amount of changes in regulatory requirements.
Dialogue-act-driven Conversation Model : An Experimental Study
The utility of additional semantic information for the task of next utterance selection in an automated dialogue system is the focus of study in this paper.
A Deep Generative Framework for Paraphrase Generation
In this paper, we address the problem of generating paraphrases automatically.
Dialogue Act Sequence Labeling using Hierarchical encoder with CRF
Dialogue Act recognition associate dialogue acts (i. e., semantic labels) to utterances in a conversation.
Multitask Learning for Sequence Labeling Tasks
Our method learns multiple models, one model for each label sequence.
Learning Multiple Tasks using Manifold Regularization
We present a novel method for multitask learning (MTL) based on {\it manifold regularization}: assume that all task parameters lie on a manifold.
