Simplified Neural Unsupervised Domain Adaptation
Table 2: Summary of results. B=Books, D=Dvd, E=Electronics,K=Kitchen. AE-SCL=Reported results from Ziser and Reichart (2017). AE-SCLR=Replicated results from the same. JointMI=results from the joint model in Section 3.1. JointO=results from the joint model using oracle MI pivot selection. Bold indicates a significant difference (p<0.05) between AE-SCLR and JointMI using Welch’s one-tailed t-test.
['S→T', 'AE-SCL', 'AE-SCL [ITALIC] R', '[ITALIC] JointMI', '[ITALIC] JointO']
First, we note that our replication of AE-SCL improves upon their reported results in 8 of 12 pairs, often by substantial margins, and is only worse in one pair (Kitchen→Books). Our JointMI method is superior to the reported AE-SCL results in all pairs, 1.7 points (absolute) on average, and significantly better than the AE-SCLR in 9 of 12 pairs, using Welch’s one-tailed t-test. This is, to our knowledge, the best result on this task using a feature-based approach (i.e., excluding systems that use embeddings). The JointOracle configuration shows that, despite the large gains of joint training, there is still significant improvement available with better pivot selection.