Papers by Noura Al Moubayed
On Isotropy, Contextualization and Learning Dynamics of Contrastive-based Sentence Representation Learning (2023.findings-acl)
Copied to clipboard
| Challenge: | Incorporating contrastive learning objectives in sentence representation learning has yielded significant improvements on many sentence-level NLP tasks. |
| Approach: | They aim to examine why contrastive learning works for learning sentence-level semantics . they interpret successes through the geometry of the representation shifts based on isotropy . |
| Outcome: | The proposed model improves on many sentence-level NLP tasks, but it is not well understood why it works. |
Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task (2022.lrec-1)
Copied to clipboard
| Challenge: | Numerical tables are widely used to communicate or report the classification performance of machine learning models with respect to a set of evaluation metrics. |
| Approach: | They propose a task where neural models are trained to generate textual explanations based on the metrics’ scores reported in numerical tables. |
| Outcome: | The proposed model outperforms existing methods and can be used to explain the performance of ML models. |
Analyzing LLMs’ Knowledge Boundary Cognition Across Languages Through the Lens of Internal Representations (2025.acl-long)
Copied to clipboard
| Challenge: | Understanding the knowledge boundaries of Large Language Models (LLMs) is crucial to prevent hallucination, but research on the knowledge boundary perceptions of LLMs has predominantly focused on English. |
| Approach: | They propose a training-free alignment method that effectively transfers knowledge boundary perception ability across languages, thereby helping reduce hallucination risk in low-resource languages. |
| Outcome: | The proposed method reduces hallucination risk in low-resource languages by fine-tuning on bilingual question pair translation. |
Length is a Curse and a Blessing for Document-level Semantics (2023.emnlp-main)
Copied to clipboard
| Challenge: | In recent years, contrastive learning (CL) has been extensively utilized to recover sentence and document-level encoding capability from pre-trained language models. |
| Approach: | They propose a document-based contrastive learning framework that is length-agnostic self-reference based on document length. |
| Outcome: | The proposed framework achieves state-of-the-art on the standard information retrieval benchmark. |
MuLD: The Multitask Long Document Benchmark (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing benchmarks for NLP focus on tasks for one or two sentences, but efficient techniques are needed for processing much longer sequences. |
| Approach: | They propose to modify existing NLP tasks to create a long document benchmark which requires models to successfully model long-term dependencies in the text. |
| Outcome: | The proposed benchmark is much more challenging than its ‘short document’ equivalents. |
SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval (2024.findings-acl)
Copied to clipboard
Siwei Wu, Yizhi Li, Kang Zhu, Ge Zhang, Yiming Liang, Kaijing Ma, Chenghao Xiao, Haoran Zhang, Bohao Yang, Wenhu Chen, Wenhao Huang, Noura Al Moubayed, Jie Fu, Chenghua Lin
| Challenge: | Multi-modal information retrieval (MMIR) is a rapidly evolving field . current benchmarks for image-text pairings overlook the scientific domain . |
| Approach: | They develop a scientific domain-specific MMIR benchmark to evaluate image-text pairings using open-access research paper corpora. |
| Outcome: | The proposed benchmarks are based on 530K image-text pairs extracted from scientific documents with detailed captions. |
X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation (2026.findings-acl)
Copied to clipboard
Kun Zhao, Chenghao Xiao, Sixing Yan, Haoteng Tang, William K. Cheung, Noura Al Moubayed, Liang Zhan, Chenghua Lin
| Challenge: | Technical language and templated nature of professional reports hinder patient comprehension and allow models to artificially boost lexical metrics such as BLEU by reproducing common report patterns. |
| Approach: | They propose a layman's RRG framework that leverages layperson-friendly language to enhance patient accessibility and promote robust evaluation and report generation by encouraging models to focus on semantic accuracy over rigid templates. |
| Outcome: | The proposed framework improves model performance with more layman-style data, compared to templated professional language and inflated lexical scores. |