What’s in a Domain? Learning Domain-Robust Text Representations using Adversarial Training
Table 2: Accuracy [%] of different models over five in-domain datasets using cross-validation evaluation and macro-averaged accuracy (“ALLin”).
['Models', 'Deb', 'JRCA', 'Wiki', 'ClWb', 'RCV2', 'ALLin']
Our proposed methods (Cond +d and Cond +d+g) consistently achieve better performance than the baseline. Both Cond and Gen achieve competitive performance with the state-of-the-art langid.py in the in-domain scenario. Although langid.py performs slightly better on average accuracy, our best model outperforms langid.py for three of the five datasets.