多领域文本分类的最大批量Frobenius范数
Maximum Batch Frobenius Norm for Multi-Domain Text Classification
摘要：
深度学习的出现使多领域文本分类取得了显著的成就。最近，许多努力致力于应用对抗学习来提取域不变特征，以产生最先进的结果。然而，这些方法仍然面临一个挑战：将原始特征变换为域不变的会扭曲原始特征的分布，降低学习特征的可分辨性。为了解决这个问题，我们首先研究了批量分类输出矩阵的结构，并从理论上证明了学习特征的可分辨性与批量输出矩阵的Frobenius范数具有正相关性。基于这一发现，我们提出了一个最大批量Frobenius范数（MBF）的方法，以提高MDTC的特征鉴别力。两个MDTC基准测试的实验表明，我们的MBF方法可以有效地提高性能的国家的最先进的。
英文摘要：
Multi-domain text classification (MDTC) has obtained remarkable achievements due to the advent of deep learning. Recently, many endeavors are devoted to applying adversarial learning to extract domain-invariant features to yield state-of-the-art results. However, these methods still face one challenge: transforming original features to be domain-invariant distorts the distributions of the original features, degrading the discriminability of the learned features. To address this issue, we first investigate the structure of the batch classification output matrix and theoretically justify that the discriminability of the learned features has a positive correlation with the Frobenius norm of the batch output matrix. Based on this finding, we propose a maximum batch Frobenius norm (MBF) method to boost the feature discriminability for MDTC. Experiments on two MDTC benchmarks show that our MBF approach can effectively advance the performance of the state-of-the-art.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
