Improving cross-lingual model transfer by chunking
Table 2: Variation of average chunker accuracy and UAS of chunk-level transfer over 6 languages with training set size with English and Hindi as source languages
['Training set size', 'Avg. chunking acc.(%)', 'Avg. acc. (En as src.)', 'Avg. acc. (Hi as src.)']
We experimented with different sizes of dataset for training the chunkers. We observe that the accuracy increases with training set size and stabilizes beyond a training set of 500 sentences. we present the variation of the average UAS over the 9 target languages with training set sizes of the chunkers used to predict the chunks. We observe the beyond a training set size of 50 the average performance of chunk-level transfer parser improves over the performance on the word-level transfer. We also observe that the performance stabilizes at about a chunker training size of 500 sentences. In the following discussion with English as source language we report the results corresponding to the chunkers trained with 500 instances.