43 citations · 67 across the 29 of their papers we have counts for
9 papers · 1 filter
Unsupervised Summarization Re-ranking
Mathieu Ravaut, Shafiq Joty, Nancy Chen
With the rise of task-specific pre-training objectives, abstractive summarization models like PEGASUS offer appealing zero-shot performance on downstream summarization tasks. Howev…
Are Current Task-oriented Dialogue Systems Able to Satisfy Impolite Users?
Zhiqiang Hu, Roy Kaa-Wei Lee, Nancy F. Chen
Task-oriented dialogue (TOD) systems have assisted users on many tasks, including ticket booking and service inquiries. While existing TOD systems have shown promising performance…
CoHS-CQG: Context and History Selection for Conversational Question Generation
Xuan Long Do, Bowei Zou, Liangming Pan +3
Conversational question generation (CQG) serves as a vital task for machines to assist humans, such as interactive reading comprehension, through conversations. Compared to traditi…
Towards Summary Candidates Fusion
Mathieu Ravaut, Shafiq Joty, Nancy F. Chen
Sequence-to-sequence deep neural models fine-tuned for abstractive summarization can achieve great performance on datasets with enough human annotations. Yet, it has been shown tha…
Multi-Document Summarization with Centroid-Based Pretraining
Ratish Puduppully, Parag Jain, Nancy F. Chen +1
In Multi-Document Summarization (MDS), the input can be modeled as a set of documents, and the output is its summary. In this paper, we focus on pretraining objectives for MDS. Spe…
Multimodal Dialogue State Tracking
Hung Le, Nancy F. Chen, Steven C. H. Hoi
Designed for tracking user goals in dialogues, a dialogue state tracker is an essential component in a dialogue system. However, the research of dialogue state tracking has largely…
Domain-specific Language Pre-training for Dialogue Comprehension on Clinical Inquiry-Answering Conversations
Zhengyuan Liu, Pavitra Krishnaswamy, Nancy F. Chen
There is growing interest in the automated extraction of relevant information from clinical dialogues. However, it is difficult to collect and construct large annotated resources f…
Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style Transfer
Zhengyuan Liu, Nancy F. Chen
Text style transfer is an important task in controllable language generation. Supervised approaches have pushed performance improvement on style-oriented rewriting such as formalit…
SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization
Mathieu Ravaut, Shafiq Joty, Nancy F. Chen
Sequence-to-sequence neural networks have recently achieved great success in abstractive summarization, especially through fine-tuning large pre-trained language models on the down…