Papers by Minh Le
XTRA: Cross-Lingual Topic Modeling with Topic and Representation Alignments (2025.findings-emnlp)
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| Challenge: | XTRA aims to uncover shared semantic themes across languages . previous methods have achieved improvements in topic diversity but struggle to ensure high topic coherence and consistent alignment across languages. |
| Approach: | a new framework unifies Bag-of-Words modeling with multilingual embeddings is proposed to address this problem . XTRA introduces two core components: (1) representation alignment and (2) topic alignment to enforce cross-lingual consistency. |
| Outcome: | XTRA outperforms baselines in topic coherence, diversity, and alignment quality on multilingual corpora. |
VIMQA: A Vietnamese Dataset for Advanced Reasoning and Explainable Multi-hop Question Answering (2022.lrec-1)
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| Challenge: | Existing Vietnamese Question Answering (QA) datasets do not explore the model’s ability to perform advanced reasoning and provide evidence to explain the answer. |
| Approach: | They propose to use Vietnamese as a question-answer dataset with 10,000 Wikipedia-based multi-hop question-and-answ pairs to test model's ability to reason and explain the answer. |
| Outcome: | The proposed dataset is in Vietnamese, a low-resource language. |
A Deep Dive into Word Sense Disambiguation with LSTM (C18-1)
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| Challenge: | LSTM-based language models have been shown effective in Word Sense Disambiguation (WSD) but neither the training data nor the source code was released. |
| Approach: | They propose to use LSTM-based language models to perform Word Sense Disambiguation (WSD) using openly available datasets and software. |
| Outcome: | The proposed method returned state-of-the-art performance in several benchmarks, but neither the training data nor the source code were released. |
CodeWiki: Evaluating AI’s Ability to Generate Holistic Documentation for Large-Scale Codebases (2026.findings-acl)
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| Challenge: | Existing automated methods struggle to capture rich semantic dependencies and architectural structure. |
| Approach: | They propose a framework for automated repository-level documentation across seven programming languages. |
| Outcome: | The proposed framework outperforms the closed-source DeepWiki benchmark by 68.79% and is open source to support future research. |
Neural Models of Selectional Preferences for Implicit Semantic Role Labeling (L18-1)
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| Challenge: | Existing studies on implicit semantic role labeling have been limited due to the lack of training data. |
| Approach: | They propose to use more complex machine learning models trained on a large amount of explicit roles to recover implicit roles. |
| Outcome: | The proposed models outperform baseline models on ON5V dataset, but have mostly negative results . they show that multi-way selectional preference improves results for predicting explicit semantic roles, but harms performance for implicit roles. |