Papers by Linh Tran
Universal Proposition Bank 2.0 (2022.lrec-1)
Copied to clipboard
Ishan Jindal, Alexandre Rademaker, Michał Ulewicz, Ha Linh, Huyen Nguyen, Khoi-Nguyen Tran, Huaiyu Zhu, Yunyao Li
| Challenge: | Semantic role labeling (SRL) is a shallow semantic parsing task that identifies "who did what to whom when, where etc." SRL is useful in a wide range of downstream NLP tasks and real-world applications. |
| Approach: | They propose a method to generate shallow semantic parsing tasks using monolingual SRL and multilingual parallel data. |
| Outcome: | The proposed method improves the quality of the generated propbanks. |
Lifelong Event Detection via Optimal Transport (2024.emnlp-main)
Copied to clipboard
| Challenge: | Continual event detection (CED) is a challenging task due to catastrophic forgetting, where learning new tasks hampers performance on previous ones. |
| Approach: | They propose a method that leverages optimal transport principles to align the optimization of a classification module with the intrinsic nature of each class, as defined by their pre-trained language modeling. |
| Outcome: | The proposed method outperforms state-of-the-art methods on MAVEN and ACE datasets and is a pioneering solution in continual event detection. |
Extracting Symptoms and their Status from Clinical Conversations (P19-1)
Copied to clipboard
| Challenge: | Existing models for extracting symptoms from clinical conversations are inherently difficult. |
| Approach: | They propose two new deep learning models tailored for a new application . they propose a hierarchical span-attribute tagging model and a sequence-to-sequence model . |
| Outcome: | The proposed models perform well under different conditions and are compared to existing models. |
Preserving Generalization of Language models in Few-shot Continual Relation Extraction (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for Few-shot Continual Relations Extraction (FCRE) are limited in labeled training data and models must learn from a few new samples to solve new tasks. |
| Approach: | They propose a method that leverages often-discarded language model heads to integrate knowledge from new relations with limited labeled data while avoiding catastrophic forgetting. |
| Outcome: | The proposed method circumvents catastrophic forgetting and preserves prior knowledge from pre-trained backbones while maintaining accuracy of existing classifications. |
Learning to Infer Entities, Properties and their Relations from Clinical Conversations (D19-1)
Copied to clipboard
| Challenge: | Existing relation extraction models restrict inferring relations between tokens within a few neighboring sentences to avoid high computational complexity. |
| Approach: | They propose a Span Attribute Tagging (SAT) model to infer clinical entities and their properties using a hierarchical two-stage approach. |
| Outcome: | The proposed model outperforms baseline models in identifying relations between symptoms and properties by about 32% and 50% on medications and their properties. |
The Medical Scribe: Corpus Development and Model Performance Analyses (2020.lrec-1)
Copied to clipboard
Izhak Shafran, Nan Du, Linh Tran, Amanda Perry, Lauren Keyes, Mark Knichel, Ashley Domin, Lei Huang, Yu-hui Chen, Gang Li, Mingqiu Wang, Laurent El Shafey, Hagen Soltau, Justin Stuart Paul
| Challenge: | Existing tools to assist in clinical note generation using audio of provider-patient encounters are lacking. |
| Approach: | They develop an annotation scheme to extract relevant clinical concepts from audio of provider-patient encounters and train a state-of-the-art tagging model. |
| Outcome: | The proposed model is more useful than the F-scores reflect and can be used in clinical notes. |
Transforming Brainwaves into Language: EEG Microstates Meet Text Embedding Models for Dementia Detection (2025.acl-srw)
Copied to clipboard
| Challenge: | Dementia is recognised as the seventh leading cause of mortality globally and plays a major role in increasing disability and dependence among older adults. |
| Approach: | They propose to represent electroencephalography microstates as symbolic, language-like sequences and use text embedding and time-series deep learning models for classification. |
| Outcome: | The proposed method achieves a high accuracy of 94.31% on 1001 EEG data from multiple countries and eliminates fixed configurations and costly/invasive modalities. |