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Wei-Jen Ko

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2 papers

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AAAI Conference 2019 Conference Paper

Domain Agnostic Real-Valued Specificity Prediction

Wei-Jen Ko
Greg Durrett
Junyi Jessy Li

Sentence specificity quantifies the level of detail in a sentence, characterizing the organization of information in discourse. While this information is useful for many downstream applications, specificity prediction systems predict very coarse labels (binary or ternary) and are trained on and tailored toward specific domains (e. g. , news). The goal of this work is to generalize specificity prediction to domains where no labeled data is available and output more nuanced realvalued specificity ratings. We present an unsupervised domain adaptation system for sentence specificity prediction, specifically designed to output real-valued estimates from binary training labels. To calibrate the values of these predictions appropriately, we regularize the posterior distribution of the labels towards a reference distribution. We show that our framework generalizes well to three different domains with 50%-68% mean absolute error reduction than the current state-of-the-art system trained for news sentence specificity. We also demonstrate the potential of our work in improving the quality and informativeness of dialogue generation systems.

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AAAI Conference 2017 Conference Paper

Learning Deep Latent Space for Multi-Label Classification

Chih-Kuan Yeh
Wei-Chieh Wu
Wei-Jen Ko
Yu-Chiang Frank Wang

Multi-label classiﬁcation is a practical yet challenging task in machine learning related ﬁelds, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classiﬁcation, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales conﬁrm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classiﬁcation.

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