When applying self-training to a parser trained with a small dataset we expect the coverage of the parser to increase, since the combined training set should contain items that the seed dataset does not.
since the accuracy of annotation of such a parser is poor (see the no self-training curve in Figure 1) the combined training set surely includes inaccurate labels that might harm parser performance.