Paraphrase-Supervised Models of Compositionality
Table 1: Number of paraphrase examples per POS pair type out of the two-to-one word paraphrases in the small version of PPDB (using the Penn Treebank tag-set). We distinguish between the five most common POS pair types, and group the remaining pairs into the generic other category.
['POS Pair', 'Size']
The training data is filtered to provide only two-to-one word paraphrase mappings, and the multiword portion of the paraphrase is subsequently POS-tagged. Given the lack of context when tagging, it is likely that the POS tagger yields the most probable tag for words and not the most probable tag given the (limited) context. Furthermore, even the higher quality portions of PPDB yield paraphrases of ranging quality, ranging from non-trivial mappings such as young people → youth, to redundant ones like the ceasefire → ceasefire. However, PPDB-like resources are more easily available than human-annotated resources (in multiple languages too: \newciteGanitkevich2014), so it is imperative that methods which learn compositional functions from such sources handle noisy supervision adequately.