Agreement-based Learning of Parallel Lexicons and Phrases from Non-Parallel Corpora
Table 2: Effect of noise in terms of F1 on the development set.
['noise C', 'noise E', 'C → E', 'E → C', 'Outer', 'Inner']
In row 1, “0+0” denotes there is no noise, which can be seen as an upper bound. Adding noise, either on the Chinese side or on the English side, deteriorates the F1 scores for all methods. Adding noise on the English side makes predicting phrase alignment in the C → E direction more challenging due to the enlarged search space. The situation is similar in the reverse direction. It is clear that agreement-based learning is more robust to noise: while independent training suffers from a reduction of 40% in terms of F1 for the “20K + 20K” setting, agreement-based learning still achieves F1 scores over 70%.