Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing
Table 2: Results on WebQuestions test dataset.
['Method', 'avg P.', 'avg R.', 'avg F1']
We get similar results on the test data as we reported on the development data. Again, the ppdb model performs best with an F1 score of 47.7. The baselines, original and mt, lag with scores of 45.0 and 47.1, respectively. We also present the results of existing literature on this dataset. Among these, \newciteberant_semantic_2014 also uses paraphrasing but unlike ours it is based on a template grammar (containing 8 grammar rules) and requires logical forms beforehand to generate paraphrases. Our ppdb outperforms Berant and Liang ’s model by 7.8 F1 points. Note that we use GraphParser as our semantic parsing framework for evaluating our paraphrases extrinsically. We leave plugging our paraphrases to other existing methods and other tasks for future work.