2 citations · 5 across the 16 of their papers we have counts for
18 papers
Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues
Chuyuan Li, Patrick Huber, Wen Xiao +3
Discourse processing suffers from data sparsity, especially for dialogues. As a result, we explore approaches to build discourse structures for dialogues, based on attention matric…
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…
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…
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…