1 Introduction
Table 12: Spearman’s correlation for bigram similarity dataset. Results for other models are taken from Wang et al. (2015)
['Model', 'AN', 'VN', 'Avg.']
Following Wang et al. , we fix the dimensionality of the vectors at L = 384. Other hyperparameters are tuned using the tuning data. CorrNet performs better than the previous state-of-the-art (DCCAE) on average score. Best results are obtained using CorrNet-500-384. This experiment suggests that apart from multiview applications such as (i) transfer learning (ii) reconstructing missing view and (iii) matching items across views, CorrNet can also be employed to exploit multiview data to improve the performance of a single view task (such as monolingual bigram similarity).