2024-08-26
【學術亮點】Handover QG: 基於解碼器融合與強化學習之問句生成技術
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【學術亮點】Handover QG: 基於解碼器融合與強化學習之問句生成技術
AI core Technology: Advanced Research and Resource Integration Platform or AI Technology【Department of Computer Science and Engineering / Yao-Chung Fan / Associate Professor】
核心技術：AI核心技術之進階研究與資源整合平台【資訊工程學系范耀中副教授】
上架日期：2024/7/10
AI core Technology: Advanced Research and Resource Integration Platform or AI Technology【Department of Computer Science and Engineering / Yao-Chung Fan / Associate Professor】
核心技術：AI核心技術之進階研究與資源整合平台【資訊工程學系范耀中副教授】
| 論文篇名 | 英文：Handover QG: Question Generation by Decoder Fusion and Reinforcement Learning 中文：Handover QG: 基於解碼器融合與強化學習之問句生成技術 |
| 期刊名稱 | IEEE/ACM Transactions on Audio, Speech, and Language Processing |
| 發表年份, 卷數, 起迄頁數 | 2024, Vol. 32, pp. 3644-3655. |
| 作者 | Ho-Lam Chung, Ying-Hong Chan, Yao-Chung Fan(范耀中)∗ |
| DOI | 10.1109/TASLP.2024.3426292 |
| 中文摘要 | 近年來，問題生成（QG）作為一項研究主題，受到了廣泛關注，特別是在其支援自動閱讀理解評量準備的潛力方面。然而，目前的QG模型大多是基於事實型數據集進行訓練，這些數據集生成的問題往往過於簡單，無法有效評估進階能力。一個具前景的替代方案是將QG模型訓練於考試類型的數據集上，這些數據集包含需要內容推理的問題。不過，相較於事實型問題，這類訓練數據相對稀少。為了解決這個問題並提升QG在生成進階問題方面的質量，我們提出了Handover QG框架。該框架結合了考試類型QG和事實型QG的聯合訓練，並透過交替使用考試類型QG解碼器與事實型QG解碼器來控制問題生成過程。此外，我們採用強化學習來增強QG的表現。我們的實驗評估結果顯示，我們的模型顯著優於其他基準模型，BLEU-4分數從5.31提升至6.48。人工評估也證實了我們模型生成的問題不僅具有可回答性，難度也適中。總結來說，Handover QG框架提供了一個具潛力的解決方案，能夠有效提升QG在生成進階閱讀理解評量問題方面的表現。 |
| 英文摘要 | In recent years, Question Generation (QG) has gained significant attention as a research topic, particularly in the context of its potential to support automatic reading comprehension assessment preparation. However, current QG models are mostly trained on factoid-type datasets, which tend to produce questions that are too simple for assessing advanced abilities. One promising alternative is to train QG models on exam-type datasets, which contain questions that require content reasoning. Unfortunately, there is a shortage of such training data compared to factoid-type questions. To address this issue and improve the quality of QG for generating advanced questions, we propose the Handover QG framework. This framework involves the joint training of exam-type QG and factoid-type QG, and controls the question generation process by interleavingly using the exam-type QG decoder and the factoid-type QG decoder. Furthermore, we employ reinforcement learning to enhance QG performance. Our experimental evaluation shows that our model significantly outperforms the compared baselines, with a BLEU-4 score increase from 5.31 to 6.48. Human evaluation also confirms that the questions generated by our model are answerable and appropriately difficult. Overall, the Handover QG framework offers a promising solution for improving QG performance in generating advanced questions for reading comprehension assessment. |
| 發表成果與AI計畫研究主題相關性 | 問句生成模型與問答模型為一體兩面之關聯。良好的問句生成模型將有助於問答模型藉由訓練資料之增量來提升效能。IEEE/ACM Transactions on Audio, Speech, and Language Processing 為自然語言處理研究中最頂級的期刊，我們利用多重Decoder模型，混合多資料集來進行問句生成模型之設計。神農TAIDE也可藉由此問句生成技術進一步強化。 |
