Explaining Graph Neural Networks predictions to end users of AI applications in easily understandable terms remains an unsolved problem.
no code implementations • • Arvind Agarwal, Laura Chiticariu, Poornima Chozhiyath Raman, Marina Danilevsky, Diman Ghazi, Ankush Gupta, Shanmukha Guttula, Yannis Katsis, Rajasekar Krishnamurthy, Yunyao Li, Shubham Mudgal, Vitobha Munigala, Nicholas Phan, Dhaval Sonawane, Sneha Srinivasan, Sudarshan R. Thitte, Mitesh Vasa, Ramiya Venkatachalam, Vinitha Yaski, Huaiyu Zhu
Contracts are arguably the most important type of business documents.
Ontology Alignment is an important research problem applied to various fields such as data integration, data transfer, data preparation, etc.
Ontology Alignment is an important research problem that finds application in various fields such as data integration, data transfer, data preparation etc.
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 officers responsible for maintaining adherence constantly struggle to keep up with the large amount of changes in regulatory requirements.
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.
Dialogue Act recognition associate dialogue acts (i. e., semantic labels) to utterances in a conversation.