TableCoder: Table Extraction from Text via Reliable Code Generation.
| Authors | Haoyu Dong 0001 et al. |
| Year | 2025 |
| Venue | ACL 2025 |
| Paper | View on DBLP |
Abstract
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Engineering Breakdown
Plain English
TableCoder addresses the problem of extracting structured tables from unstructured text documents, which is critical for data pipelines that need to convert documents into queryable databases. The paper proposes a code generation approach where the system generates executable code (likely Python or SQL) that reliably extracts and formats table data from text, rather than trying to directly predict table structures. This method appears to improve extraction accuracy and robustness compared to traditional sequence-to-sequence table extraction models, particularly for complex or malformed source documents.
Core Technical Contribution
The core novelty is reframing table extraction as a code generation problem, where the model learns to generate reliable, executable code that performs the extraction and transformation logic rather than predicting structured table outputs directly. This approach leverages the inherent correctness-checking properties of code execution — generated code either runs successfully and produces valid output or fails clearly, providing a natural verification mechanism that reduces hallucination and invalid outputs. The technique treats code as an intermediate representation that bridges natural language understanding and structured data extraction, allowing the model to reason about the extraction logic explicitly rather than implicitly through neural networks.
How It Works
The system takes raw text or semi-structured documents as input and uses a language model (likely a fine-tuned transformer) to generate Python or SQL code that, when executed, produces the desired table output. The model is trained on pairs of (input document, correct extraction code) where the code demonstrates the logic needed to parse and structure the data. During inference, the generated code is executed in a controlled sandbox environment, and the actual executed output becomes the final table extraction, not just the raw model output. This adds a verification step — if code doesn't compile or throws an error, the system can either retry generation or fall back to alternative extraction strategies, making the pipeline more robust than end-to-end neural predictions.
Production Impact
This approach significantly improves reliability in document processing pipelines where extraction errors cascade through downstream tasks. Teams building invoice processing, contract extraction, or financial document systems would benefit from the built-in correctness guarantees of code execution — you get verifiable, debuggable extraction logic instead of opaque neural predictions. However, you need to provision a secure code execution environment (sandboxing), which adds operational complexity and latency (code generation and execution is slower than direct inference). The trade-off is worth it for high-stakes extraction tasks where false positives are costly, but may be overkill for high-volume, low-error-tolerance scenarios where simpler neural extraction suffices.
Limitations and When Not to Use This
The approach assumes that extraction logic can be expressed programmatically, which breaks down for highly ambiguous or context-dependent table structures where no deterministic algorithm exists. Code generation also requires more sophisticated model capabilities and larger, higher-quality training data of (document, code) pairs compared to direct table prediction, making it more expensive to build custom extractors. The method may struggle with novel table formats or domain-specific structures not well-represented in training data, since the generated code must be syntactically and semantically correct to execute. Additionally, sandboxing code execution introduces latency and resource overhead that may not be acceptable for real-time extraction pipelines.
Research Context
This work builds on the recent success of code generation models in tasks like math reasoning (Codex, GPT-4) and semantic parsing, where intermediate code serves as a more interpretable and verifiable representation than end-to-end neural outputs. It extends the table extraction research line (prior work includes seq2seq models for table generation and pre-trained models like TAPAS) by introducing executable verification rather than relying solely on token-level accuracy. The paper likely contributes to the broader emerging paradigm of treating code as a reasoning medium for structured prediction tasks, opening the direction of exploring which extraction and data transformation problems benefit most from explicit code generation versus implicit neural prediction.
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