22 citations · 35 across the 11 of their papers we have counts for
11 papers
Multi-Modal Video Topic Segmentation with Dual-Contrastive Domain Adaptation
Linzi Xing, Quan Tran, Fabian Caba +5
Video topic segmentation unveils the coarse-grained semantic structure underlying videos and is essential for other video understanding tasks. Given the recent surge in multi-modal…
Tracing Influence at Scale: A Contrastive Learning Approach to Linking Public Comments and Regulator Responses
Linzi Xing, Brad Hackinen, Giuseppe Carenini
U.S. Federal Regulators receive over one million comment letters each year from businesses, interest groups, and members of the public, all advocating for changes to proposed regul…
Diversity-Aware Coherence Loss for Improving Neural Topic Models
Raymond Li, Felipe González-Pizarro, Linzi Xing +2
The standard approach for neural topic modeling uses a variational autoencoder (VAE) framework that jointly minimizes the KL divergence between the estimated posterior and prior, i…
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…
Predicting Above-Sentence Discourse Structure using Distant Supervision from Topic Segmentation
Patrick Huber, Linzi Xing, Giuseppe Carenini
RST-style discourse parsing plays a vital role in many NLP tasks, revealing the underlying semantic/pragmatic structure of potentially complex and diverse documents. Despite its im…
Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation
Raymond Li, Wen Xiao, Linzi Xing +3
The multi-head self-attention mechanism of the transformer model has been thoroughly investigated recently. In one vein of study, researchers are interested in understanding why an…
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…