62 citations · 130 across the 40 of their papers we have counts for
8 papers · 1 filter
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…
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…
Mixture-of-Linguistic-Experts Adapters for Improving and Interpreting Pre-trained Language Models
Raymond Li, Gabriel Murray, Giuseppe Carenini
In this work, we propose a method that combines two popular research areas by injecting linguistic structures into pre-trained language models in the parameter-efficient fine-tunin…
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…
Personalized Abstractive Summarization by Tri-agent Generation Pipeline
Wen Xiao, Yujia Xie, Giuseppe Carenini +1
Tailoring outputs from large language models, like ChatGPT, to implicit user preferences remains a challenge despite their impressive generative capabilities. In this paper, we pro…
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions
Rindranirina Ramamonjison, Timothy T. Yu, Raymond Li +8
The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text…
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…