Papers by Mengxia Yu
Pre-training Language Models for Comparative Reasoning (2023.emnlp-main)
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| Challenge: | Comparative reasoning is a process of comparing objects, concepts, or entities to draw conclusions. |
| Approach: | They propose a framework to pre-train language models for enhancing comparative reasoning abilities . they collect scalable data for text-based entity comparison . |
| Outcome: | The proposed framework significantly improves comparative reasoning abilities under low-resource conditions on downstream tasks. |
Faceted Hierarchy: A New Graph Type to Organize Scientific Concepts and a Construction Method (D19-53)
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| Challenge: | faceted concept hierarchy is a structure of parent-child relationships . concepts are expected to be organized in a hierarchical structure for student learning . |
| Approach: | They propose a faceted concept hierarchy that aims to build facets from scientific literature. |
| Outcome: | The proposed hierarchy is more complete than "type-of" relations, and resolves conflicts by maintaining the acyclic structure of a hierarchy. |
A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods (2023.eacl-main)
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| Challenge: | Multi-task learning is a popular approach in natural language processing because of its commonalities and differences. |
| Approach: | They propose to summarize recent advances in multi-task learning methods based on their task relatedness into two general multi-step training methods. |
| Outcome: | The proposed methods summarize the tasks and discuss future directions. |
Reference-based Metrics Disprove Themselves in Question Generation (2024.findings-emnlp)
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| Challenge: | Existing metrics for question generation are based on human-written references . however, the results of the metrics on our study disprove the metrics themselves . |
| Approach: | They propose to use human-written references to evaluate question generation . they propose to combine criteria such as naturalness, answerability, and complexity . |
| Outcome: | The proposed model is based on multi-dimensional criteria such as naturalness, answerability, and complexity, utilizing large language models. |
QG-SMS: Enhancing Test Item Analysis via Student Modeling and Simulation (2025.acl-long)
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| Challenge: | Question Generation (QG) tasks are often evaluated using reference-based metrics such as ROUGE and BLEU. |
| Approach: | They propose a QG evaluation framework that leverages Large Language Model for Student Modeling and Simulation to perform test item analysis. |
| Outcome: | The proposed framework improves the QG task and human-simulated student profiles. |
Tri-Train: Automatic Pre-Fine Tuning between Pre-Training and Fine-Tuning for SciNER (2020.findings-emnlp)
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| Challenge: | Pre-training a language model by self-supervised tasks on huge datasets and fine-tuning with small labelled data are often inadequate for scientific NER tasks. |
| Approach: | They propose to introduce a "pre-fine tuning" step between pre-training and fine-tuning to construct a corpus by selecting sentences from unlabeled documents that are the most relevant with labelled training data. |
| Outcome: | The proposed approach improves on seven benchmarks on the performance of the proposed model on labelled datasets. |