Cross-lingual Transfer of Twitter Sentiment Models Using a Common Vector Space
Table 4: The expansion of training sets with instances from several languages projected into the common vector space using the LASER library (left-hand side) and comparison with training and testing set from the same language (on the right-hand side).
['Source', 'Target', 'Expanded ¯¯¯¯¯¯ [ITALIC] F1', 'Expanded CA', 'Only target ¯¯¯¯¯¯ [ITALIC] F1', 'Only target CA']
These results indicate that the tested expansion of datasets was unsuccessful, i.e. the provided amount of instances from the target language was already sufficient for successful learning. The additional instances from other languages are likely to be of lower quality then the native instances and therefore decrease the performance.