I-Bin Liao


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Location-Aware Visual Question Generation with Lightweight Models
Nicholas Suwono | Justin Chen | Tun Hung | Ting-Hao Huang | I-Bin Liao | Yung-Hui Li | Lun-Wei Ku | Shao-Hua Sun
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

This work introduces a novel task, location-aware visual question generation (LocaVQG), which aims to generate engaging questions from data relevant to a particular geographical location. Specifically, we represent such location-aware information with surrounding images and a GPS coordinate. To tackle this task, we present a dataset generation pipeline that leverages GPT-4 to produce diverse and sophisticated questions. Then, we aim to learn a lightweight model that can address the LocaVQG task and fit on an edge device, such as a mobile phone. To this end, we propose a method which can reliably generate engaging questions from location-aware information. Our proposed method outperforms baselines regarding human evaluation (e.g., engagement, grounding, coherence) and automatic evaluation metrics (e.g., BERTScore, ROUGE-2). Moreover, we conduct extensive ablation studies to justify our proposed techniques for both generating the dataset and solving the task.


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預訓練詞向量模型應用於客服對話系統意圖偵測之研究(Study on Pre-trained Word Vector Model Applied to Intent Detection of Customer Service Dialogue System)
Guan-Yu Chen | Min-Feng Kuo | Tsung-Hsien Yang | Chun-Hsun Chen | I-Bin Liao
Proceedings of the 31st Conference on Computational Linguistics and Speech Processing (ROCLING 2019)


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資源受限運算環境下華英混雜語音辨識系統 (Mandarin/English Mixed-Lingual Speech Recognition System on Resource-Constrained Platforms) [In Chinese]
Wei-Tyng Hong | Hong-Ci Chen | I-Bin Liao | Wern-Jun Wang
Proceedings of the 21st Conference on Computational Linguistics and Speech Processing