43 citations · 67 across the 29 of their papers we have counts for
31 papers
Picking the Underused Heads: A Network Pruning Perspective of Attention Head Selection for Fusing Dialogue Coreference Information
Zhengyuan Liu, Nancy F. Chen
The Transformer-based models with the multi-head self-attention mechanism are widely used in natural language processing, and provide state-of-the-art results. While the pre-traine…
Multi-label and Multi-target Sampling of Machine Annotation for Computational Stance Detection
Zhengyuan Liu, Hai Leong Chieu, Nancy F. Chen
Data collection from manual labeling provides domain-specific and task-aligned supervision for data-driven approaches, and a critical mass of well-annotated resources is required t…
CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation
Minzhi Li, Taiwei Shi, Caleb Ziems +4
Annotated data plays a critical role in Natural Language Processing (NLP) in training models and evaluating their performance. Given recent developments in Large Language Models (L…
Instructive Dialogue Summarization with Query Aggregations
Bin Wang, Zhengyuan Liu, Nancy F. Chen
Conventional dialogue summarization methods directly generate summaries and do not consider user's specific interests. This poses challenges in cases where the users are more focus…
PromptSum: Parameter-Efficient Controllable Abstractive Summarization
Mathieu Ravaut, Hailin Chen, Ruochen Zhao +3
Prompt tuning (PT), a parameter-efficient technique that only tunes the additional prompt embeddings while keeping the backbone pre-trained language model (PLM) frozen, has shown p…
Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation
Taha Aksu, Min-Yen Kan, Nancy F. Chen
A challenge in the Dialogue State Tracking (DST) field is adapting models to new domains without using any supervised data, zero-shot domain adaptation. Parameter-Efficient Transfe…
Multiple output samples per input in a single-output Gaussian process
Jeremy H. M. Wong, Huayun Zhang, Nancy F. Chen
The standard Gaussian Process (GP) only considers a single output sample per input in the training set. Datasets for subjective tasks, such as spoken language assessment, may be an…
Guiding Computational Stance Detection with Expanded Stance Triangle Framework
Zhengyuan Liu, Yong Keong Yap, Hai Leong Chieu +1
Stance detection determines whether the author of a piece of text is in favor of, against, or neutral towards a specified target, and can be used to gain valuable insights into soc…
Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer
Zhiqiang Hu, Roy Ka-Wei Lee, Nancy F. Chen
Adapting a large language model for multiple-attribute text style transfer via fine-tuning can be challenging due to the significant amount of computational resources and labeled d…
Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation
Xuan Long Do, Bowei Zou, Shafiq Joty +4
Conversational Question Generation (CQG) is a critical task for machines to assist humans in fulfilling their information needs through conversations. The task is generally cast in…
Decomposed Prompting for Machine Translation Between Related Languages using Large Language Models
Ratish Puduppully, Anoop Kunchukuttan, Raj Dabre +2
This study investigates machine translation between related languages i.e., languages within the same family that share linguistic characteristics such as word order and lexical si…
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…
Improving Multi-Party Dialogue Discourse Parsing via Domain Integration
Zhengyuan Liu, Nancy F. Chen
While multi-party conversations are often less structured than monologues and documents, they are implicitly organized by semantic level correlations across the interactive turns,…
DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing
Zhengyuan Liu, Ke Shi, Nancy F. Chen
Text discourse parsing weighs importantly in understanding information flow and argumentative structure in natural language, making it beneficial for downstream tasks. While previo…
Text2Brain: Synthesis of Brain Activation Maps from Free-form Text Query
Gia H. Ngo, Minh Nguyen, Nancy F. Chen +1
Most neuroimaging experiments are under-powered, limited by the number of subjects and cognitive processes that an individual study can investigate. Nonetheless, over decades of re…
Controllable Neural Dialogue Summarization with Personal Named Entity Planning
Zhengyuan Liu, Nancy F. Chen
In this paper, we propose a controllable neural generation framework that can flexibly guide dialogue summarization with personal named entity planning. The conditional sequences a…