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Found 9 papers, 3 papers with code
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.
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.
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.
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.
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.
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
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.
