Leveraging Newswire Treebanks for Parsing Conversational Data with Argument Scrambling
Table 3: Accuracy of our different parsing models on conversational data as well as newswire evaluation sets. Improvements in superscript are over the newswire baseline.
['Data-set', '[BOLD] Newswire [ITALIC] PG/ [ITALIC] UD  [BOLD] Gold POS', '[BOLD] Newswire [ITALIC] PG/ [ITALIC] UD  [BOLD] Gold POS', '[BOLD] Newswire [ITALIC] PG/ [ITALIC] UD  [BOLD] Auto POS', '[BOLD] Newswire [ITALIC] PG/ [ITALIC] UD  [BOLD] Auto POS', '[BOLD] Newswire [ITALIC] PG/ [ITALIC] UD+Transformed Newswire [ITALIC] PG/ [ITALIC] UD  [BOLD] Gold POS', '[BOLD] Newswire [ITALIC] PG/ [ITALIC] UD+Transformed Newswire [ITALIC] PG/ [ITALIC] UD  [BOLD] Gold POS', '[BOLD] Newswire [ITALIC] PG/ [ITALIC] UD+Transformed Newswire [ITALIC] PG/ [ITALIC] UD  [BOLD] Auto POS', '[BOLD] Newswire [ITALIC] PG/ [ITALIC] UD+Transformed Newswire [ITALIC] PG/ [ITALIC] UD  [BOLD] Auto POS', '[BOLD] Newswire [ITALIC] UD+English [ITALIC] UD  [BOLD] Gold POS', '[BOLD] Newswire [ITALIC] UD+English [ITALIC] UD  [BOLD] Gold POS', '[BOLD] Newswire [ITALIC] UD+English [ITALIC] UD  [BOLD] Auto POS', '[BOLD] Newswire [ITALIC] UD+English [ITALIC] UD  [BOLD] Auto POS']
We ran two experiments to evaluate the effectiveness of the tree transformations on the parsing of conversational data. In the first, we leverage the monolingual annotations by applying syntactic transformations; in the second we use a cross-lingual treebank with diverse word-orders. For each experiment type, we report results using both predicted and gold POS tags. The POS taggers are trained using an architecture similar to the parser’s with a single layer MLP which takes its input from Bi-LSTM representation of the focus word (see Appendix for the results). We used the newswire parsing models as the baseline for evaluating the impact of tree transformations and multilingual annotations. The augmented models are trained on the union of the original newswire training data and the transformed trees.