Zero-Shot Cross-Lingual Opinion Target Extraction
Table 2: Zero-shot results for cross-lingual learning from multiple source languages to a target language. The row best→target represents the best performing cross-lingual model from Figure 2 for each target language. all others→target are the results for training on all languages except for the target language. target→target shows the monolingual scores that act as a baseline.
['target', 'en', 'es', 'nl', 'ru', 'tr']
In order to answer our second research question, we train a model on the available training data for all but one language and perform prediction on the test data for the left-out language. We can see that all languages with the exception of Turkish seem to profit from a cross-lingual transfer setting with multiple source languages. The absolute improvements are in the range of 6 to 8 points in F1-Score while the performance on Turkish samples drops by 3 points. Since our model can leverage the data from all languages simultaneously, we can exhaust our resources and train an instance of our model that has access to all training data samples from all languages, including the target training data. We see, however, that the model cannot leverage the other source languages beyond what it achieves with the combination of the full target and English language data alone.