Papers by Ximing Li
Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts (2021.findings-emnlp)
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| Challenge: | Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models. |
| Approach: | They develop a neural topic model which extracts topics from word co-occurrence graphs . Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models. |
| Outcome: | Empirical results show that the proposed model can generate more coherent topics than baseline topic models. |
Semi-Supervised Text Classification with Balanced Deep Representation Distributions (2021.acl-long)
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| Challenge: | Semi-Supervised Text Classification (SSTC) is a type of self-training that uses labeled and unlabeled data to perform certain applications. |
| Approach: | They propose a method to initialize a deep classifier by training over labeled texts . they then alternatively predict unlabeled texts as their pseudo-labels and train them over the mixture . |
| Outcome: | Empirical results show that the proposed method is more accurate when labeled texts are scarce. |
Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction (2023.acl-long)
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| Challenge: | sarcasm is a form of irony conveying mockery and contempt . social media has become increasingly popular for identifying sarcasm . |
| Approach: | They develop a method to detect sarcasm from social media using augmented potentials. |
| Outcome: | The proposed method outperforms baselines on benchmark datasets. |
A Pseudo Label based Dataless Naive Bayes Algorithm for Text Classification with Seed Words (C18-1)
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| Challenge: | Existing supervised text classifications require a large number of manually labeled documents. |
| Approach: | They develop a pseudo-label based dataless Naive Bayes classifier with seed words . they initialize pseudo-labels for each document using seed word occurrences . |
| Outcome: | The proposed classifier outperforms traditional supervised text classification algorithms with seed words on an imbalanced dataset. |
A Contrastive Cross-Channel Data Augmentation Framework for Aspect-Based Sentiment Analysis (2022.coling-1)
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| Challenge: | Aspect-based sentiment analysis is sensitive to multi-aspect challenges, resulting in multiple aspects in a sentence. |
| Approach: | They propose a framework that leverages an in-domain generator to construct more multi-aspect samples . they then boost the robustness of ABSA models via contrastive learning on these generated samples ." |
| Outcome: | The proposed framework outperforms baselines without any augmentations on accuracy and Macro- F1 . the proposed framework can generate more multi-aspect samples and boost the robustness of ABSA models . |
On the Step Length Confounding in LLM Reasoning Data Selection (2026.findings-acl)
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Bing Wang, Rui Miao, Chen Shen, Shaotian Yan, Kaiyuan Liu, Ximing Li, Xiaosong Yuan, Sinan Fan, Jun Zhang, Jieping Ye
| Challenge: | Existing pipelines generate long reasoning data from more capable Large Language Models (LLMs) and apply manually heuristic or naturalness-based selection methods to filter high-quality samples. |
| Approach: | They propose to use supervised fine-tuning to generate long reasoning data from more capable Large Language Models and apply manually heuristic or naturalness-based selection methods to filter high-quality samples. |
| Outcome: | Experiments on four LLMs and five evaluation benchmarks show that the proposed approach is effective in mitigating step length confounding problem. |
In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search (2024.emnlp-main)
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Huihan Li, Yuting Ning, Zeyi Liao, Siyuan Wang, Xiang Li, Ximing Lu, Wenting Zhao, Faeze Brahman, Yejin Choi, Xiang Ren
| Challenge: | Logic-Induced-Knowledge-Search (LINK) is a framework for generating factually-correct yet long-tail inferential knowledge. |
| Approach: | They introduce a framework to obtain factually-correct yet long-tail inferential statements using variable-wise prompting grounded on symbolic rules. |
| Outcome: | The proposed framework is able to obtain factually-correct yet long-tail inferential statements while ensuring factual correctness. |
Switching Heads and Softening Tokens: Turnkey Solutions to Visually Grounded Document QA (2026.findings-acl)
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| Challenge: | Document Question Answering lacks robust, end-to-end solutions capable of handling complex, multi-answer queries without reliance on ad-hoc processing. |
| Approach: | They propose a single-head architecture where coordinates are represented as special tokens within the unified vocabulary. |
| Outcome: | The proposed architectures improve visual grounding but lack spatial precision bound by discretization. |