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Found 6 papers, 1 papers with code
Getting Better Dialogue Context for Knowledge Identification by Leveraging Document-level Topic Shift
To build a goal-oriented dialogue system that can generate responses given a knowledge base, identifying the relevant pieces of information to be grounded in is vital.
Multi-task Learning in Argument Mining for Persuasive Online Discussions
We utilize multi-task learning to improve argument mining in persuasive online discussions, in which both micro-level and macro-level argumentation must be taken into consideration.
Enhancing Knowledge Retrieval with Topic Modeling for Knowledge-Grounded Dialogue
In this work, we propose an approach that utilizes topic modeling on the knowledge base to further improve retrieval accuracy and as a result, improve response generation.
Utilizing Natural Language Processing for Automated Assessment of Classroom Discussion
Rigorous and interactive class discussions that support students to engage in high-level thinking and reasoning are essential to learning and are a central component of most teaching interventions.
Layer-stacked Attention for Heterogeneous Network Embedding
Additionally, by successively stacking layer representations, the learned node embedding offers a more interpretable aggregation scheme for nodes of different types at different neighborhood ranges.
Network Representation of Large-Scale Heterogeneous RNA Sequences with Integration of Diverse Multi-omics, Interactions, and Annotations Data
We proposed a novel deep learning-based framework, rna2rna, which learns from RNA sequences to produce a low-dimensional embedding that preserves the proximities in both the interactions topology and the functional affinity topology.
