15 citations · 20 across the 7 of their papers we have counts for
9 papers
Visual Analytics for Generative Transformer Models
Raymond Li, Ruixin Yang, Wen Xiao +3
While transformer-based models have achieved state-of-the-art results in a variety of classification and generation tasks, their black-box nature makes them challenging for interpr…
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…
T3-Vis: a visual analytic framework for Training and fine-Tuning Transformers in NLP
Raymond Li, Wen Xiao, Lanjun Wang +2
Transformers are the dominant architecture in NLP, but their training and fine-tuning is still very challenging. In this paper, we present the design and implementation of a visual…
W-RST: Towards a Weighted RST-style Discourse Framework
Patrick Huber, Wen Xiao, Giuseppe Carenini
Aiming for a better integration of data-driven and linguistically-inspired approaches, we explore whether RST Nuclearity, assigning a binary assessment of importance between text s…
Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning
Linzi Xing, Wen Xiao, Giuseppe Carenini
In news articles the lead bias is a common phenomenon that usually dominates the learning signals for neural extractive summarizers, severely limiting their performance on data wit…
Predicting Discourse Trees from Transformer-based Neural Summarizers
Wen Xiao, Patrick Huber, Giuseppe Carenini
Previous work indicates that discourse information benefits summarization. In this paper, we explore whether this synergy between discourse and summarization is bidirectional, by i…