14 citations · 30 across the 9 of their papers we have counts for
11 papers
MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective
Zhe Hu, Hou Pong Chan, Lifu Huang
Teaching neural models to generate narrative coherent texts is a critical problem. Recent pre-trained language models have achieved promising results, but there is still a gap betw…
PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation
Zhe Hu, Hou Pong Chan, Jiachen Liu +3
Despite recent progress of pre-trained language models on generating fluent text, existing methods still suffer from incoherence problems in long-form text generation tasks that re…
Grounding Commands for Autonomous Vehicles via Layer Fusion with Region-specific Dynamic Layer Attention
Hou Pong Chan, Mingxi Guo, Cheng-Zhong Xu
Grounding a command to the visual environment is an essential ingredient for interactions between autonomous vehicles and humans. In this work, we study the problem of language gro…
Controllable Summarization with Constrained Markov Decision Process
Hou Pong Chan, Lu Wang, Irwin King
We study controllable text summarization which allows users to gain control on a particular attribute (e.g., length limit) of the generated summaries. In this work, we propose a no…
Dialogue Summarization with Supporting Utterance Flow Modeling and Fact Regularization
Wang Chen, Piji Li, Hou Pong Chan +1
Dialogue summarization aims to generate a summary that indicates the key points of a given dialogue. In this work, we propose an end-to-end neural model for dialogue summarization…
A Condense-then-Select Strategy for Text Summarization
Hou Pong Chan, Irwin King
Select-then-compress is a popular hybrid, framework for text summarization due to its high efficiency. This framework first selects salient sentences and then independently condens…