Contextual Lensing of Universal Sentence Representations
Table 5: F1 scores for parallel data mining on the BUCC 2018 training and test sets. The first four language columns are training while the last four are testing. Middle group are unsupervised mining. For training data we report oracle scores with the corresponding mean and variance of thresholds (τ). For test data, we report results using the best threshold found on the training sets. Best results overall are bolded, best results per group are underlined.
['Model Schwenk ( 2018 )', 'DE 76.1', 'FR 74.9', 'RU 73.3', 'ZH 71.6', 'DE 76.9', 'FR 75.8', 'RU 73.8', 'ZH 71.6', 'Mean( [ITALIC] τ)', 'Std( [ITALIC] τ)']
First observe the mean and variance of the threshold parameters. This is even true for unsupervised models, meaning one could tune the threshold on high-resource pairs and use the same model for mining language pairs without seeing any parallel data. Again, we see strong performance from our Simple encoder, which only learns a single weight matrix. In this experiment, we notice a more substantial performance difference between our best results and LASER. We made a single submission of our best model to Zweigenbaum et al.