Abstract
Semantic parsing from denotations faces two key challenges in model training: (1) given only the denotations (e.g., answers), search for good candidate semantic parses, and (2) choose the best model update algorithm. We propose effective and general solutions to each of them. Using policy shaping, we bias the search procedure towards semantic parses that are more compatible to the text, which provide better supervision signals for training. In addition, we propose an update equation that generalizes three different families of learning algorithms, which enables fast model exploration. When experimented on a recently proposed sequential question answering dataset, our framework leads to a new state-of-the-art model that outperforms previous work by 5.0% absolute on exact match accuracy.
Highlights
Semantic parsing from denotations (SpFD) is the problem of mapping text to executable formal representations in a situated environment and executing them to generate denotations, in the absence of access to correct representations
We describe how the commonly used algorithms are very similar – their update rules can all be viewed as special cases of the proposed generalized update equation
Maximum Margin Reward For every training example (xi, ti, zi), the maximum margin reward method finds the highest scoring program yi that evaluates to zi, as the reference program, from the set K of programs generated by the search
Summary
Semantic parsing from denotations (SpFD) is the problem of mapping text to executable formal representations (or program) in a situated environment and executing them to generate denotations (or answer), in the absence of access to correct representations. The existing learning approaches for SpFD perform two steps for every training example, a search step that explores the space of programs. We address two key challenges in model training for SpFD by proposing a novel learning framework, improving both the search and update steps. The search step is complicated by spurious programs, which happen to evaluate to the correct answer but do not represent accurately the meaning of the natural language question. For the environment, the program Select Nation Where Name = Karen Andrew is spurious. Selecting spurious programs as positive examples can greatly affect the performance of semantic parsers as these programs generally do not gen-. In order to generate the program, the DynSP parser (Iyyer et al, 2017) will take the first action as adding the SQL expression Select Nation. We assume that it is a Boltzmann policy, meaning that p✓(y | x, t) / exp{score✓(y, x, t)}
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