## Paper: A Generative Model for Parsing Natural Language to Meaning Representations (67/365)

Today’s paper once again constructs a semantic parser. The parser is trained on sentences paired with their meaning representations. But there is no finer labeling of the correspondence between words and meaning tokens.

The meaning representation in this paper takes the form of a tree whose nodes have both natural language words and meaning representation tokens. They say that the meaning representation is variable-free but I have to trace another reference to see what that precisely means (for later). An example meaning representation is shown below

Every node in the tree takes on the following form where $X_1$ to $X_k$ are also semantic cateogries. Some examples are ‘River: largest(River)’ and ‘Num: count(State)’.

$\displaystyle \text{semantic category} \rightarrow \text{function}(X_1, \dots, X_k)$

A hybrid tree is an extension of this tree that captures both the sentence and the meaning representation. The only difference is that every node can also emit NL tokens. The leaves of an MR are always NL tokens. Generation of a tree is viewed as a Markov process where 1) we start with a root production, 2) and we recursively expand its parameters, 3) at each node we can emit NL tokens.

Unlike in a PCFG parsing task where the correspondence between NL words and syntactic structures is available, the current model does not have access to this data. Thus, we need to compute the expected parameters from all possible tree derivations. The authors adapt the inside-outside algorithm for this purpose. I won’t go into the details at this point. Again, I’ll wait to link it with other papers.

I kind of lost interest towards the end because there is an extra re-ranking phase after finding the most likely hybrid-tree for a given sentence. Anyway, as this is a paper is pretty old (2008), let’s see what more recent papers do.

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