For this week's reading I read the required paper and the following related paper:
Unsupervised Semantic Parsing, Hoifung Poon, Pedro Domingos, EMNLP 2009
Both have as goal mapping sentences to logical form, but their approaches and settings are very different. I will highlight some key differences between the related paper and the required paper.
Setting: Supervised versus unsupervised. Kwiatkowski et al. use as training data sentences with corresponding logical representations. Poon et al.’s approach is unsupervised.
Approach: Kwiatkowski use a top-down approach. They start with logical forms that map sentences completely. These forms are then iteratively refined with a restricted higher-order unification procedure. Poon et al. use a bottom-up approach. They start with lambda-form clusters at the atom level and then recursively build up larger clusters using two operations (merge and compose).
What they learn: Kwiatkowski et al. learn a CCG grammar (thus both syntax as well as semantics). Poon et al. only focus on semantics, and use an existing parser (Stanford parser) for the syntax.
The nice thing about Kwiatkowski et al. approach is that it's more general than previous work (can handle different languages and meaning representations), while still having comparable performance compared with less general approaches.
What I liked about Poon’s approach is the idea of clustering to account for syntactic variations of the same meaning. However, Poon et al.'s work was more difficult to evaluate, because no gold standard was available. They therefore performed a task-based evaluation (question answering) and they compared their approach with information extraction systems. Because of their evaluation setup, their performance on semantic parsing was less clear to me.