Search Results for author:
Found 7 papers, 0 papers with code
Unsupervised Extractive Summarization with Learnable Length Control Strategies
Different from the centrality-based ranking methods, our extractive scorer can be trained in an end-to-end manner, with no other requirement of positional assumption.
Prompt-Based Length Controlled Generation with Reinforcement Learning
Large language models (LLMs) like ChatGPT and GPT-4 have attracted great attention given their surprising performance on a wide range of NLP tasks.
Enhancing Coherence of Extractive Summarization with Multitask Learning
This study proposes a multitask learning architecture for extractive summarization with coherence boosting.
Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures
It is extended from the existing study on differentiable neural architecture search, and we made the backbone architecture transformable rather than fixed during the training process.
Adaptive Hierarchical Hyper-gradient Descent
In this study, we investigate learning rate adaption at different levels based on the hyper-gradient descent framework and propose a method that adaptively learns the optimizer parameters by combining multiple levels of learning rates with hierarchical structures.
Regularized Flexible Activation Function Combinations for Deep Neural Networks
Based on this, a novel family of flexible activation functions that can replace sigmoid or tanh in LSTM cells are implemented, as well as a new family by combining ReLU and ELUs.
COMBINED FLEXIBLE ACTIVATION FUNCTIONS FOR DEEP NEURAL NETWORKS
Based on this, we develop two novel flexible activation functions that can be implemented in LSTM cells and auto-encoder layers.
