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Found 9 papers, 3 papers with code
Power-Law Graph Cuts
Despite strong performance for a number of clustering tasks, spectral graph cut algorithms still suffer from several limitations: first, they require the number of clusters to be known in advance, but this information is often unknown a priori; second, they tend to produce clusters with uniform sizes.
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network
Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context.
Ranked #6 on
Conversational Response Selection
on RRS
Proactive Human-Machine Conversation with Explicit Conversation Goals
DuConv enables a very challenging task as the model needs to both understand dialogue and plan over the given knowledge graph.
Proactive Human-Machine Conversation with Explicit Conversation Goal
Konv enables a very challenging task as the model needs to both understand dialogue and plan over the given knowledge graph.
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension
In this paper, we introduce a simple system Baidu submitted for MRQA (Machine Reading for Question Answering) 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models.
How to Evaluate the Next System: Automatic Dialogue Evaluation from the Perspective of Continual Learning
Automatic dialogue evaluation plays a crucial role in open-domain dialogue research.
Distilling Knowledge from Pre-trained Language Models via Text Smoothing
As an alternative, we propose a new method for BERT distillation, i. e., asking the teacher to generate smoothed word ids, rather than labels, for teaching the student model in knowledge distillation.
