Data Recombination for Neural Semantic Parsing (1606.03622v1)

Abstract: Modeling crisp logical regularities is crucial in semantic parsing, making it difficult for neural models with no task-specific prior knowledge to achieve good results. In this paper, we introduce data recombination, a novel framework for injecting such prior knowledge into a model. From the training data, we induce a high-precision synchronous context-free grammar, which captures important conditional independence properties commonly found in semantic parsing. We then train a sequence-to-sequence recurrent network (RNN) model with a novel attention-based copying mechanism on datapoints sampled from this grammar, thereby teaching the model about these structural properties. Data recombination improves the accuracy of our RNN model on three semantic parsing datasets, leading to new state-of-the-art performance on the standard GeoQuery dataset for models with comparable supervision.

• The paper introduces a novel data recombination technique that constructs new training examples by recombining segments of logical forms and utterances.
• The paper demonstrates significant improvements in parsing accuracy, with notable F1 score gains on standard semantic parsing datasets.
• The paper highlights practical benefits including reduced annotation costs and promising avenues for enhanced transfer learning in NLP.

An Analysis of "Data Recombination for Neural Semantic Parsing"

The paper "Data Recombination for Neural Semantic Parsing" by Robin Jia and Percy Liang addresses the challenges inherent in neural semantic parsing. The central focus lies in advancing data augmentation techniques to improve parsing performance, particularly when annotated datasets are limited.

Problem Statement

Semantic parsing involves converting natural language into structured logical forms. Traditional approaches rely heavily on large amounts of annotated data, which is not always feasible. This research aims to address the scarcity of annotated data by exploring data recombination techniques that can enhance the performance of neural semantic parsers.

Methodology

The authors propose a novel data recombination technique that constructs new examples by recombining parts of existing ones. This method effectively generates a more diverse set of training instances, facilitating better generalization. The recombination process involves splitting logical forms and corresponding utterances and recombining them to construct new, meaningful data pairs.

Experiments and Results

Experiments were conducted using standard benchmarks in semantic parsing, including the ATIS and GeoQuery datasets. The data recombination approach demonstrated a significant improvement in parsing accuracy compared to baseline models that did not employ recombination. For instance, the application of data recombination led to an increase in F1 scores, evidencing the effectiveness of the proposed method in enhancing parser performance even with limited data.

Implications

The implications of this research are manifold:

• Practical: The ability to enhance performance with limited data can reduce the cost and effort associated with data annotation, making semantic parsing more accessible and feasible across various applications.
• Theoretical: This work contributes to the understanding of how data augmentation techniques can be leveraged effectively in the domain of semantic parsing and suggests potential for similar techniques in other areas of NLP.

Future Developments

This research sets the stage for further exploration into automated data recombination processes and their integration with other machine learning paradigms. One possible avenue is the examination of recombination schemes that consider contextual semantic correctness more deeply. Additionally, the implications for transfer learning where recombined data from one domain could augment training in another remain an intriguing possibility.

In conclusion, the paper presents a compelling case for the use of data recombination in neural semantic parsing, offering both a theoretical framework and practical results that underscore the potential of this approach in enhancing NLP models where data limitations are a core challenge.