  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.
