Incorporating External Knowledge through Pre-training for Natural Language to Code Generation (2004.09015v1)

Abstract: Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents. Motivated by the intuition that developers usually retrieve resources on the web when writing code, we explore the effectiveness of incorporating two varieties of external knowledge into NL-to-code generation: automatically mined NL-code pairs from the online programming QA forum StackOverflow and programming language API documentation. Our evaluations show that combining the two sources with data augmentation and retrieval-based data re-sampling improves the current state-of-the-art by up to 2.2% absolute BLEU score on the code generation testbed CoNaLa. The code and resources are available at https://github.com/neulab/external-knowledge-codegen.

• The paper presents a two-stage pre-training method that integrates mined NL-code pairs and API documentation to enhance code generation.
• It employs a model-agnostic framework, yielding a 2.2% improvement in BLEU scores from 30.1 to 32.3 on the CoNaLa benchmark.
• The study underscores the importance of external knowledge in filling gaps left by manual curation and guides future research on scalable pre-training.

Incorporating External Knowledge through Pre-training for Natural Language to Code Generation

The paper "Incorporating External Knowledge through Pre-training for Natural Language to Code Generation" investigates enhancements in the process of generating code in general-purpose programming languages, such as Python, from natural language intents. This field, often referred to as semantic parsing for open-domain code generation, has gained significant attention as it has moved from domain-specific spaces to more general-purpose applications.

Methodology

The research addresses the challenge of generating code that not only adheres to correct syntax but also makes appropriate API and library calls to achieve intended functionalities. A pivotal aspect of the work is the consideration of external resources that developers often consult, such as online forums and API documentation, to augment the data available for training models.

To implement these insights, the authors proposed a methodology that leverages automatically generated data from external sources for pre-training models, followed by fine-tuning with manually curated datasets. The external knowledge sources comprise:

1. Mined NL-code Pairs: A large corpus of natural language and code pairs mined from StackOverflow is utilized. The quality of these pairs is determined via a classifier that assesses the likelihood of a valid correspondence between the natural language intent and the code snippet.
2. API Documentation: The paper taps into Python API documentation by transforming it into NL-code pairs. This involves extracting code signatures and descriptions and using heuristics to simulate real-world developer queries in natural language.

The incorporation of these sources is effectuated through a model-agnostic two-stage training strategy: an initial pre-training on larger, potentially noisier datasets followed by fine-tuning on a smaller, quality-controlled dataset.

Results

Experiments conducted on the CoNaLa benchmark indicate that the proposed approach outperforms existing state-of-the-art models, increasing BLEU scores from 30.1 to 32.3. Detailed results show a 2.2% absolute improvement over previous methods, a notable enhancement in a challenging domain.

The paper also details the mechanics of various strategies used to sample from API documentation, correcting for distributional shifts between documentation and real-world usage patterns. The resampling and retrieval techniques are essential to ensure the pre-training phase best represents the subsequent domain-specific fine-tuning phase.

Implications and Future Work

This research underscores the importance of external knowledge integration in improving the efficacy of NL-to-code generation models. Practically, incorporating these resources can bridge gaps in knowledge coverage that purely curated datasets leave open, especially as manual annotation remains a costly process. Theoretically, it suggests new paths in leverage model architectures capable of integrating domain-specific knowledge cues into broader general-purpose learning frameworks.

Future developments could extend this work through the incorporation of a wider array of external knowledge sources and investigating zero-shot learning scenarios. Further research could also explore the use of automatic execution of generated code against predefined test cases for more robust evaluation metrics.

Overall, the paper makes a significant contribution to the field of code generation by illustrating how external knowledge, which is typically accessed informally by human developers, can be systematically integrated into the training workflows of artificial intelligence models to enhance their performance.