2 citations · 5 across the 11 of their papers we have counts for
5 papers · 1 filter
Unsupervised Learning of Discourse Structures using a Tree Autoencoder
Patrick Huber, Giuseppe Carenini
Discourse information, as postulated by popular discourse theories, such as RST and PDTB, has been shown to improve an increasing number of downstream NLP tasks, showing positive e…
Do We Really Need That Many Parameters In Transformer For Extractive Summarization? Discourse Can Help !
Wen Xiao, Patrick Huber, Giuseppe Carenini
The multi-head self-attention of popular transformer models is widely used within Natural Language Processing (NLP), including for the task of extractive summarization. With the go…
Unleashing the Power of Neural Discourse Parsers -- A Context and Structure Aware Approach Using Large Scale Pretraining
Grigorii Guz, Patrick Huber, Giuseppe Carenini
RST-based discourse parsing is an important NLP task with numerous downstream applications, such as summarization, machine translation and opinion mining. In this paper, we demonst…
From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation
Patrick Huber, Giuseppe Carenini
Sentiment analysis, especially for long documents, plausibly requires methods capturing complex linguistics structures. To accommodate this, we propose a novel framework to exploit…
MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision
Patrick Huber, Giuseppe Carenini
The lack of large and diverse discourse treebanks hinders the application of data-driven approaches, such as deep-learning, to RST-style discourse parsing. In this work, we present…