Papers by Minh Le Nguyen
Causal Intersectionality and Dual Form of Gradient Descent for Multimodal Analysis: A Case Study on Hateful Memes (2024.lrec-main)
Copied to clipboard
| Challenge: | Causal analyses define semantics, while gradient-based methods are essential to eXplainable AI (XAI), interpreting the model’s ‘black box’. |
| Approach: | They propose to integrate causal analysis and XAI to integrate a model's mechanisms into their analysis by integrating a dataset of hateful meme detection models. |
| Outcome: | The proposed model can detect hateful memes using intersectionality principles and summarized attention scores highlight distinct behaviors of three Transformer models. |
Document-Level Neural Machine Translation Using BERT as Context Encoder (2020.aacl-srw)
Copied to clipboard
| Challenge: | Large-scale pre-trained representations such as BERT have been widely used in many natural language understanding tasks. |
| Approach: | They propose to use BERT as a context encoder to achieve document-level contextual information, which is then integrated into both the encoder and decoder. |
| Outcome: | The proposed model outperforms strong document-level machine translation baselines on BLEU score and captures document- level context information to boost translation performance. |
PolyMinder: A Support System for Entity Annotation and Relation Extraction in Polymer Science Documents (2025.coling-demos)
Copied to clipboard
| Challenge: | Automated Named Entity Recognition (NER) and Relation Extraction (RE) models are tailored to the polymer domain. |
| Approach: | They propose to automate the annotation process by providing a web-based interface where users can visualize, verify, and refine the extracted information before finalizing the annotations. |
| Outcome: | The proposed system streamlines the annotation process by providing a web-based interface where users can visualize, verify, and refine the extracted information before finalizing the annotations. |
OptiPrune: Effective Pruning Approach for Every Target Sparsity (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for model pruning only perform optimally within specific sparsity ranges. |
| Approach: | They propose a pruning method that reduces model size by eliminating redundant parameters . they compare it with OptiPrune, which adapts non-uniform sparsity with adaptive deviation . |
| Outcome: | The proposed method reduces model size and maintains performance despite large size and high computational demands. |
Answering Legal Questions by Learning Neural Attentive Text Representation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods for retrieval-based question answering are limited by legal documents and long and complicated documents. |
| Approach: | They propose a retrieval-based model for answering legal questions at the article level by learning neural attentive text representation. |
| Outcome: | The proposed model outperforms state-of-the-art retrieval-based methods on an annotated corpus of 5,922 Vietnamese legal questions in terms of recall and NDCG. |