Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars (1207.1420v1)

Abstract: This paper addresses the problem of mapping natural language sentences to lambda-calculus encodings of their meaning. We describe a learning algorithm that takes as input a training set of sentences labeled with expressions in the lambda calculus. The algorithm induces a grammar for the problem, along with a log-linear model that represents a distribution over syntactic and semantic analyses conditioned on the input sentence. We apply the method to the task of learning natural language interfaces to databases and show that the learned parsers outperform previous methods in two benchmark database domains.

• The paper introduces a learning algorithm that integrates CCGs with a log-linear model to map sentences to lambda-calculus logical forms.
• It demonstrates over 95% precision on benchmark datasets, significantly outperforming previous natural language interface methods.
• The GENLEX mechanism automatically induces lexical entries, reducing manual lexicon creation and enhancing scalability.

Structured Classification with Probabilistic Categorial Grammars for Mapping Sentences to Logical Forms

In "Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars," Zettlemoyer and Collins present a novel algorithm to address the challenging problem of converting natural language sentences into their corresponding logical forms, specifically lambda-calculus encodings. Their approach leverages combinatory categorial grammars (CCGs) combined with a log-linear model to represent a distribution over potential syntactic and semantic analyses conditioned on the input sentence. This is primarily applied in the context of developing natural language interfaces to databases (NLIDBs).

Summary of Contributions

The paper's major contributions can be summarized as follows:

1. Algorithm and Framework: The authors propose a learning algorithm that generates a CCG to map sentences to logical forms. The CCG is augmented with probabilistic categorial grammars (PCCGs) which use a log-linear model to handle the inherent ambiguity in natural language.
2. Experimental Validation: Application to benchmark datasets, Geo880 and Jobs640, demonstrates superior performance compared to previous methods. Their method achieves over 95% precision, significantly improving the state-of-the-art.
3. Lexical Induction Mechanism: They introduce an automatic lexicon induction component, termed GENLEX, which produces candidate lexical items crucial for parsing sentences into their logical forms.

Technical Insights

Probabilistic Categorial Grammars (PCCGs)

The paper extends traditional CCGs to PCCGs by incorporating a log-linear model. The probability of a specific syntactic and semantic parse is defined using:

$$P(X, f \mid e; \theta) = \frac{e^{\theta \cdot \phi(X, f, e)}}{\sum_{(X', f')} e^{\theta \cdot \phi(X', f', e)}}$$

Here, $\phi(X, f, e)$ is a feature vector capturing various sub-structures within the parse, and $\theta$ are the parameters of the model. Parsing is approached via a dynamic programming method akin to the Viterbi algorithm, enabling efficient handling of CCGs' combinatorial complexity.

Lexicon Learning via GENLEX

A notable aspect of the proposed method is the GENLEX algorithm, responsible for generating candidate lexical entries from pairs of sentences and logical forms. GENLEX uses a set of pre-defined rules to extract subcomponents of the logical forms, generating items such as noun phrases and verb phrases that are then used in the parsing process. They manage lexicon growth by pruning redundant entries, ensuring computational feasibility and generalizability of the learned models.

Numerical Results

In the Geo880 domain, the authors report a precision of 96.25% and recall of 79.29%, while in the Jobs640 domain, the figures are 97.36% precision and 79.29% recall. These results demonstrate the method's effectiveness relative to previous approaches, such as the COCKTAIL system, which reported 89.92% precision and 79.40% recall in Geo880.

Implications and Future Directions

The implications of this research are multi-fold:

1. Improved NLIDBs: The enhanced precision and recall suggest that more reliable and accurate natural language interfaces to databases can be developed, minimizing the need for exhaustive manual lexicon creation.
2. Generalization Potential: Although focused on database query interfaces, the underlying principles can be extended to other language-understanding tasks, such as dialogue systems. Handling phenomena like anaphora and ellipses opens pathways to more robust interactive systems.
3. Scalability: The automatic lexicon induction mechanism (GENLEX) significantly reduces the dependency on domain-specific hand-crafted lexica, facilitating quicker deployment across various domains.

Conclusion

Zettlemoyer and Collins' approach to mapping natural language sentences to logical forms represents a substantial advancement in structured classification problems. By integrating CCGs with probabilistic models and introducing effective lexicon learning techniques, the paper paves the way for more sophisticated and versatile natural language understanding systems. Future exploration into larger datasets and more complex phenomena could further validate and expand the utility of this method.