Commentary for Jan. 19th, 2011

To refresh, I read the paper "Learning Context-Dependent Mappings from Sentences to Logical Form" - http://people.csail.mit.edu/mcollins/papers/acl09.pdf. Reading this paper proved relatively easier than reading the assigned paper, but also helped make clear some details that I wasn't familiar with in the assigned paper. Both papers tackle excellent problems and show relatively promising results. The assigned paper attempts to tackle more abstract and higher level problems while the paper on context-dependent mapping focuses a bit more on a single, explicit problem. By "higher level" problems I refer to such goals as successfully mapping logical forms over multiple languages, as well as discerning the meanings behind words for the purposes of non-trivial semantic analysis.

As far the context-dependent problem goes, it's really nice to see fairly simple, intuitive rules that produce generally high accuracy. The 2005 paper presents both a simple method of contextual analysis along with a bare-bones linear model to produce roughly 80% accuracy. Looking at the results, we also see that even examining just the most recent statement before a sentence, almost doubles the accuracy. Alas, the marginal benefits after looking further into the past drop almost to nothing. The assigned paper is a different kind of monster. Although it took some time to understand even just what was going on, the use of high-order unification to do the lexical splitting and the CCG to reconstruct the expression seemed pretty cool (when it started to make some sense). The approach is fairly complicated, and maybe not intuitive, but yielding high accuracy over multiple languages and meanings definitely marks progress.

-Alan Zhu