22 citations · 26 across the 4 of their papers we have counts for
6 papers
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…
Improving Unsupervised Dialogue Topic Segmentation with Utterance-Pair Coherence Scoring
Linzi Xing, Giuseppe Carenini
Dialogue topic segmentation is critical in several dialogue modeling problems. However, popular unsupervised approaches only exploit surface features in assessing topical coherence…
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…
Improving Context Modeling in Neural Topic Segmentation
Linzi Xing, Brad Hackinen, Giuseppe Carenini +1
Topic segmentation is critical in key NLP tasks and recent works favor highly effective neural supervised approaches. However, current neural solutions are arguably limited in how…
Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition
Xiaolei Huang, Linzi Xing, Franck Dernoncourt +1
Existing research on fairness evaluation of document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes. In this wo…
Evaluating Topic Quality with Posterior Variability
Linzi Xing, Michael J. Paul, Giuseppe Carenini
Probabilistic topic models such as latent Dirichlet allocation (LDA) are popularly used with Bayesian inference methods such as Gibbs sampling to learn posterior distributions over…