Coarse-to-Fine Decoding for Neural Semantic Parsing (1805.04793v1)

Abstract: Semantic parsing aims at mapping natural language utterances into structured meaning representations. In this work, we propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. Given an input utterance, we first generate a rough sketch of its meaning, where low-level information (such as variable names and arguments) is glossed over. Then, we fill in missing details by taking into account the natural language input and the sketch itself. Experimental results on four datasets characteristic of different domains and meaning representations show that our approach consistently improves performance, achieving competitive results despite the use of relatively simple decoders.

Coarse-to-Fine Decoding for Neural Semantic Parsing: A Structured Approach to Semantic Parsing Enhancement

The paper "Coarse-to-Fine Decoding for Neural Semantic Parsing" proposes an innovative approach to enhancing the neural architecture used for semantic parsing by introducing a two-stage decomposition of the decoding process. The core idea is to break down the parsing task into a coarse-to-fine sequence, where a rough sketch of the meaning is first generated, followed by filling in finer details to achieve a complete meaning representation. This structured decomposition enables the decoupling of high-level structures from low-level details, such as variable names and arguments, within the semantic parsing framework.

Methodology Overview

Semantic parsing is framed as a sequence-to-sequence problem within this research, leveraging the success of recurrent neural networks (RNNs) in natural language processing tasks. The authors employ a bi-directional LSTM with an attention mechanism to process input utterances, which are initially mapped into meaning sketches. Meaning sketches are an abstract representation, where detailed elements are glossed over. The paper uses lambda calculus-style logical forms and SQL query representations to demonstrate the approach.

The decoding leverages a two-stage model:

1. Coarse Meaning Decoder: Initially generates the structure by predicting a meaning sketch, focusing on high-level semantics without diving into specifics.
2. Fine Meaning Decoder: This stage introduces finer granularity. By using the sketches generated in the first stage and conditioning on both the initial input and the sketch, it fills in the missing details.

The architecture is designed to manage different domains, including logical form parsing, code generation, and SQL query generation. Strong numerical performance improvements were noted across all domains, signifying the broad applicability of the approach.

Experimental Results

The authors conducted experiments across multiple datasets, representing varied semantic parsing tasks: Geo, Atis, Django, and WikiSQL. The results demonstrate that the coarse-to-fine decoding approach outperforms traditional sequence-to-sequence and sequence-to-tree models, as well as challenging new baselines:

• On the Geo and Atis datasets, representing natural language to logical form parsing, the proposed model outperformed existing models, achieving accuracies of 88.2% and 87.7%, respectively.
• For the Django dataset, which translates natural language into source code, the approach attained an accuracy of 74.1%, leveraging a copying mechanism to handle out-of-vocabulary tokens.
• The WikiSQL dataset for generating SQL queries saw execution accuracy of 78.5%, outperforming several pre-existing models by making effective use of the database schema in the input encoding process.

These results underscore the efficacy of the coarse-to-fine framework, specifically highlighting improved performance when detailed sketches serve as intermediate representations.

Discussion of Implications

The proposed framework crucially demonstrates the potential for prediction improvement through structural decomposition in semantic parsing. By generating intermediate sketches, fine granularity predictions become more tractable and less error-prone. This method allows for scalability across tasks requiring the translation of natural language to structured outputs, demonstrating practicality in scenarios needing precise logical forms, executable codes, or database queries.

The authors also speculate on the potential for this approach in low-resource settings or where supervision is limited. The decoupled representation could facilitate the learning of semantic parsers from more abstract input such as question-answer pairs, thus potentially advancing the field of weakly supervised learning.

Future Directions

Moving beyond full supervision scenarios, exploring the applicability of coarse-to-fine decoding under weak supervision is a noted future endeavor. The possibilities include enhancing program synthesis challenges where accuracy in both sketch generation and detailed specification is paramount. Further, extending this paradigm to broader, more diverse datasets could offer insightful learnings on its adaptability and efficiency in real-world applications.

In conclusion, the introduction of coarse-to-fine decoding marks a significant contribution to the field of semantic parsing, illustrating enhanced accuracy and flexibility in handling various structured output forms, thereby paving the way for further exploration and refinement in adaptive neural semantic parsing architectures.