Papers by Aijun An
Generating Spatial Knowledge Graphs from Automotive Diagrams for Question Answering (2025.emnlp-industry)
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| Challenge: | Useful answers require obvious landmarks as a reference point . a decomposed pipeline is the most effective strategy for generating a high-quality SKG . |
| Approach: | They propose to generate a spatial knowledge graph from a vehicle dashboard diagram . they use large vision-language models to generate the graph using a decomposed pipeline . |
| Outcome: | The proposed method identifies landmarks with 71.3% agreement with human annotators on a new vehicle dataset. |
Learning Emotion-enriched Word Representations (C18-1)
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| Challenge: | Existing word representations based on distributional hypothesis do not provide accurate representations of emotions. |
| Approach: | They propose a method to obtain emotion-enriched word representations by remote supervision using a large training dataset of text documents and two recurrent neural network architectures. |
| Outcome: | The proposed method outperforms competing general-purpose and affective representations on two tasks. |
Question Generation Using Sequence-to-Sequence Model with Semantic Role Labels (2023.eacl-main)
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| Challenge: | Existing question generation methods that generate multiple questions from text are labor-intensive and do not capture the complexity of ways a human asks questions. |
| Approach: | They propose a question generation method that combines the benefits of rule-based and neural sequence-to-sequence (Seq2Sequen) models. |
| Outcome: | The proposed method significantly improves the state-of-the-art neural question generation approaches on three real-world data sets. |
GEAR: A Scalable and Interpretable Evaluation Framework for RAG-Based Car Assistant Systems (2025.emnlp-industry)
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Niloufar Beyranvand, Hamidreza Dastmalchi, Aijun An, Heidar Davoudi, Winston Chan, Ron DiCarlantonio
| Challenge: | Large language models (LLMs) increasingly power car assistants, but evaluating response quality remains a challenge. |
| Approach: | They propose a framework that uses large language models as evaluators to compare assistant responses against ground-truth counterparts. |
| Outcome: | The proposed framework compares assistant responses against ground-truth counterparts, assessing coverage, correctness, and other dimensions of answer quality. |
Generating Vehicular Icon Descriptions and Indications Using Large Vision-Language Models (2024.emnlp-industry)
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James Fletcher, Nicholas Dehnen, Seyed Nima Tayarani Bathaie, Aijun An, Heidar Davoudi, Ron DiCarlantonio, Gary Farmaner
| Challenge: | Existing image description systems are trained mainly on natural images, whereas icon images are drawings. |
| Approach: | They propose to use a dataset to generate both visual and functional icon descriptions based on the icon image and its context information in the car manual. |
| Outcome: | The proposed model performs well on the dashboard icon description task while the third model perform poorly. |
Neural Document Segmentation Using Weighted Sliding Windows with Transformer Encoders (2025.coling-industry)
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| Challenge: | Using overlapping text sequences and position-aware weighting, we achieve up to a 10% increase in segmentation F1 score compared to existing methods. |
| Approach: | They propose a Transformer-based method for document segmentation that utilizes overlapping text sequences with a unique position-aware weighting mechanism to enhance segmentation accuracy. |
| Outcome: | The proposed method achieves up to 10% increase in segmentation F1 score compared to existing methods and improves quality of generated responses by 5% while achieving four times greater efficiency. |
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks (2020.acl-main)
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| Challenge: | Affective tasks such as sentiment analysis, emotion classification and sarcasm detection have enjoyed great popularity in recent years. |
| Approach: | They conduct a comprehensive analysis of the role of preprocessing techniques in affective analysis based on word vector models. |
| Outcome: | The proposed model is the first of its kind and provides useful insights on the role of each preprocessing technique when applied at the training phase, commonly ignored in pretrained word vector models, and/or at the downstream task phase. |
Content-based Dwell Time Engagement Prediction Model for News Articles (N19-2)
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| Challenge: | Existing studies on article dwell time prediction are noisy and may not show the actual user engagement or satisfaction. |
| Approach: | They propose a deep neural network architecture to extract emotion, event and entity features from an article and learn interactions among them. |
| Outcome: | The proposed model outperforms state-of-the-art models on a real newspaper dataset. |
Affective and Contextual Embedding for Sarcasm Detection (2020.coling-main)
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| Challenge: | Existing methods to detect sarcasm from text lack vocal intonation or facial gestures in textual data. |
| Approach: | They propose two deep neural network models for sarcasm detection that extend the architecture of BERT by incorporating both affective and contextual features. |
| Outcome: | The proposed models outperform state-of-the-art models on different datasets with significant margins. |