Commentary for week of Jan 20

I chose the related paper "Learning for Semantic Parsing with Statistical Machine Translation" (Wong and Mooney, 2006), in which the authors utilize IBM word alignment models used in statistical machine translation to generate a lexicon for training a semantic parser. Words in each English sentence in a training set are aligned to productions in the derivation of its meaning representation in a formal language using IBM model 5. This is similar to the technique used by Kwiatkowski et al. of using IBM model 1 to initialize weights of lexical features for their parser. The differences between the two are that (1) Kwiatkowski et al. only use word alignments to initialize feature weights rather than directly using the final lexicon, and (2) IBM model 1 considers only cooccurrence statistics while model 5 considers notions of word order, distortion, and fertility. Directly using a model 5 lexicon relies more strongly on a larger set of modeling assumptions made specifically with SMT in mind. While the results shown by Wong and Mooney are favorable, the authors state in their conclusions that additional gains would likely be seen by developing alignment models with assumptions better fitting the task.