import torch
import torch.nn as nn
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

class FormatRLTranslator:
    """
    Format Reinforcement Learning for structured document translation
    Preserves XML/HTML tags while translating content
    """
    
    def __init__(self, model_name="t5-base"):
        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.structure_reward = StructurePreservationReward()
        
    def translate_with_structure(self, document_xml, target_lang="es"):
        """
        Translate document while preserving XML structure
        
        Args:
            document_xml: XML/HTML document with mixed content and tags
            target_lang: Target language code
        """
        # Parse and separate structure from content
        structure_tags, text_segments = self._parse_xml_structure(document_xml)
        
        # Translate text segments
        translated_segments = []
        for segment in text_segments:
            inputs = self.tokenizer(f"translate to {target_lang}: {segment}", 
                                   return_tensors="pt", truncation=True)
            outputs = self.model.generate(**inputs)
            translated = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
            translated_segments.append(translated)
        
        # Reconstruct with original structure
        reconstructed = self._reconstruct_document(structure_tags, translated_segments)
        
        # Calculate structure preservation reward
        reward = self.structure_reward.calculate(document_xml, reconstructed)
        
        return {
            "translated_document": reconstructed,
            "structure_preservation_score": reward,
            "original_structure": structure_tags
        }
    
    def _parse_xml_structure(self, xml_content):
        """Extract and preserve XML tag structure"""
        # Implementation of XML parsing and structure extraction
        # Returns: (list_of_tags, list_of_text_segments)
        pass
    
    def _reconstruct_document(self, tags, segments):
        """Rebuild document with translated content in original structure"""
        # Implementation of document reconstruction
        pass

# Usage example
translator = FormatRLTranslator()
result = translator.translate_with_structure(
    "1. Connect the deviceDo not immerse in water",
    target_lang="es"
)
print(f"Structure preserved: {result['structure_preservation_score']:.2%}")

Why Document Structure Matters More Than Words

Imagine translating a complex legal contract where the formatting—the numbered clauses, the indented subparagraphs, the bolded definitions—is as legally binding as the text itself. Or consider a technical manual where numbered steps, warning boxes, and code snippets must maintain their precise layout. For years, AI translation models have excelled at converting words from one language to another but have consistently failed at this crucial task of preserving document structure. They treat documents as mere sequences of sentences, stripping away the vital organizational metadata encoded in XML or HTML tags. The result? Translated documents that are linguistically correct but structurally broken, requiring expensive manual post-processing to restore their original format.

This structural blindness has confined "structured translation" to simple, sentence-level tasks, leaving complex document-level translation as a stubborn, unsolved frontier. Until now.

Introducing Format Reinforcement Learning

Researchers have proposed a novel solution detailed in a recent arXiv paper: Format Reinforcement Learning (FormatRL). This isn't just another incremental improvement to translation accuracy; it's a fundamental shift in how AI models learn to handle structured content. The core insight is simple yet powerful: if you want a model to preserve document structure, you need to reward it for doing so, directly and explicitly.

Traditional machine translation training uses a loss function that primarily optimizes for word and sentence accuracy (like BLEU or chrF++ scores). FormatRL adds a new layer of training using reinforcement learning (RL), specifically a technique called Group Relative Policy Optimization (GRPO). Think of it as giving the AI model a new report card with two additional, critical subjects: Structure and Formatting.

The Two Novel Rewards: TreeSim and Node-chrF

FormatRL introduces two bespoke reward functions that guide the model's learning:

• TreeSim: This reward measures the structural similarity between the predicted XML/HTML tree and the reference (correct) tree. It doesn't just check if tags are present; it evaluates the entire tree hierarchy—parent-child relationships, nesting depth, and sibling order. A perfectly translated document with jumbled sections would score poorly here.
• Node-chrF: This is a clever adaptation of the standard chrF++ metric for translation fluency. Instead of evaluating the entire text blob, Node-chrF assesses the translation quality within each specific XML node. It ensures that the text inside a <warning> tag or a <li> list item is accurately translated, in context.

During the RL phase, the model (the "agent") generates a translation. FormatRL then calculates these structure-aware rewards and uses GRPO to nudge the model's parameters toward actions that yield higher TreeSim and Node-chrF scores. Over time, the model internalizes the importance of structure, learning to generate translations that are both linguistically sound and format-faithful.

Why This Breakthrough Changes Everything

The implications of solving document-level structured translation are profound and extend far beyond academic benchmarks.

First, it unlocks true automation for massive, format-sensitive translation projects. Global corporations managing product documentation in dozens of languages spend millions not just on translation, but on the tedious reformatting that follows. FormatRL promises to slash those costs and timelines dramatically. Technical writing and localization teams could shift from manual cleanup to quality assurance.

Second, it enables the accurate translation of the modern web. Much of the internet's value lies in its interactive and structured elements—forms, menus, data tables, and dynamically generated content. A model trained with FormatRL could translate an entire webpage, maintaining the functional integrity of buttons, input fields, and styled components, making cross-lingual web publishing and SaaS localization seamless.

Finally, it opens the door for translating complex, nested data formats beyond HTML and XML. Think JSON configuration files, LaTeX scientific papers, or even code comments within programming languages. The paradigm of using RL to optimize for non-textual, structural fidelity is a tool that can be applied across numerous domains.

The Road Ahead: From Labs to Real-World Workflows

FormatRL, as presented, is a compelling proof-of-concept. The next steps involve rigorous validation on larger, more diverse datasets and integration into production-scale translation pipelines. Key challenges remain, such as computational cost (RL training is expensive) and handling imperfect or ambiguous reference structures.

However, the direction is clear. The future of AI translation is not just about more fluent sentences; it's about context-aware, structure-preserving document intelligence. The next generation of models will understand a document as a holistic entity—where a <title> tag, a <footnote>, and a <code block> each carry semantic and functional meaning that must be preserved across languages.

This evolution will blur the lines between translation, formatting, and content management. It suggests a future where AI doesn't just translate your words but truly translates your documents, ready for immediate use. For anyone who works with multilingual content—from global marketers and software developers to legal teams and educators—that future can't come soon enough. The era of losing structure in translation is finally coming to an end.