62 citations · 130 across the 40 of their papers we have counts for
9 papers · 1 filter
Attend to the Right Context: A Plug-and-Play Module for Content-Controllable Summarization
Wen Xiao, Lesly Miculicich, Yang Liu +2
Content-Controllable Summarization generates summaries focused on the given controlling signals. Due to the lack of large-scale training corpora for the task, we propose a plug-and…
Large Discourse Treebanks from Scalable Distant Supervision
Patrick Huber, Giuseppe Carenini
Discourse parsing is an essential upstream task in Natural Language Processing with strong implications for many real-world applications. Despite its widely recognized role, most r…
Towards Domain-Independent Supervised Discourse Parsing Through Gradient Boosting
Patrick Huber, Giuseppe Carenini
Discourse analysis and discourse parsing have shown great impact on many important problems in the field of Natural Language Processing (NLP). Given the direct impact of discourse…
Unsupervised Inference of Data-Driven Discourse Structures using a Tree Auto-Encoder
Patrick Huber, Giuseppe Carenini
With a growing need for robust and general discourse structures in many downstream tasks and real-world applications, the current lack of high-quality, high-quantity discourse tree…
Transition to Adulthood for Young People with Intellectual or Developmental Disabilities: Emotion Detection and Topic Modeling
Yan Liu, Maria Laricheva, Chiyu Zhang +5
Transition to Adulthood is an essential life stage for many families. The prior research has shown that young people with intellectual or development disabil-ities (IDD) have more…
Improving Topic Segmentation by Injecting Discourse Dependencies
Linzi Xing, Patrick Huber, Giuseppe Carenini
Recent neural supervised topic segmentation models achieve distinguished superior effectiveness over unsupervised methods, with the availability of large-scale training corpora sam…
Entity-based SpanCopy for Abstractive Summarization to Improve the Factual Consistency
Wen Xiao, Giuseppe Carenini
Despite the success of recent abstractive summarizers on automatic evaluation metrics, the generated summaries still present factual inconsistencies with the source document. In th…
Automated Utterance Labeling of Conversations Using Natural Language Processing
Maria Laricheva, Chiyu Zhang, Yan Liu +4
Conversational data is essential in psychology because it can help researchers understand individuals cognitive processes, emotions, and behaviors. Utterance labelling is a common…
Towards Understanding Large-Scale Discourse Structures in Pre-Trained and Fine-Tuned Language Models
Patrick Huber, Giuseppe Carenini
With a growing number of BERTology work analyzing different components of pre-trained language models, we extend this line of research through an in-depth analysis of discourse inf…