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# Human-in-the-Loop: Legal Knowledge Formalization in Attempto Controlled English

Grazia Garzo, Alessandro Palumbo

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Grazia Garzo, Alessandro Palumbo. Human-in-the-Loop: Legal Knowledge Formalization in Attempto Controlled English. ISDFS - 13th International Symposium on Digital Forensics and Security, Apr 2025, Boston, United States. pp.1-6, 10.1109/isdfs65363.2025.11011971. hal-05021540

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# Human-in-the-Loop: Legal Knowledge Formalization in Attempto Controlled English

1st Grazia Garzo

University of Siena, Italy

g.garzo@student.unisi.it

2nd Alessandro Palumbo

CentraleSupélec, Inria, Univ Rennes, CNRS, IRISA, France

alessandro.palumbo@inria.fr

***Abstract***—Automating legal knowledge through the use of computational languages presents a series of challenges, particularly in translating complex normative texts into formalized representations that are semantically faithful and computationally valid. A salient concern is reconciling the ambiguity inherent in natural legal language with the syntactic constraints of controlled languages. This work proposes a human-in-the-loop methodology for formalizing normative texts in the controlled language Attempto Controlled English (ACE), centered on the interaction between legal experts and computational constraints. Specifically, key legal provisions and fundamental case-law principles were translated into ACE. The findings reveal that even ostensibly straightforward legal provisions necessitate a substantial decomposition, abstraction, and adaptation process. The analysis also identified recurrent linguistic patterns (modal, temporal, and conditional) and reusable translation strategies.

***Index Terms***—Computable Law, Human-Machine Interaction, Controlled Language, Attempto Controlled English.

## I. INTRODUCTION AND MOTIVATION

In recent years, the evolution of artificial intelligence applied to the legal domain has made increasingly urgent the need to formally represent legal norms in a machine-interpretable form without compromising normative precision or legal validity [27], [29]. However, translating legal texts from natural language into a computational language faces numerous challenges: semantic ambiguity, complex syntactic structures, implicit interpretive models, and a high degree of regulatory indeterminacy [7]. In this context, controlled languages emerge as intermediary tools capable of reconciling the needs of computational formalization with the readability and intelligibility required by human operators [5], [27], [28]. In fact, natural legal language is not always accessible to non-experts. Thanks to its controlled syntax and rules that enforce a simple and linear sentence structure, Attempto Controlled English (ACE) facilitates comprehension by a broader audience. At the same time, machines interpret ACE. Indeed, the Attempto Parsing Engine (APE) [14] generates a semantic representation of sentences in the form of Discourse Representation Structures (DRSs) [20], i.e., logical structures that connect the concepts within the sentence and enable automatic reasoning.

This work lies at the intersection of legal norm representation, computational linguistics, and human-machine interaction. It proposes a human-in-the-loop methodology for the

assisted translation of European Union normative and case-law texts into ACE [31]. The case study focuses on European Directive 2009/103/EC concerning civil liability insurance arising from the use of motor vehicles [8] and its interpretation by the Court of Justice in two key rulings: Vnuk (C-162/13) and Linea Directa Aseguradora (C-100/21) [24], [30].

The main contribution of this work is the development of a prototype for an iterative ACE formalization protocol, which includes:

1) conceptual analysis and rewriting of provisions and jurisprudential principles into linguistically controlled and machine-computable sentences;

2) automatic syntactic validation using the APE parser;

3) semantic and legal verification by two legal experts to ensure consistency between the ACE formulation and the original normative meaning.

Adopting a human-in-the-loop approach allows for addressing and mitigating the structural limitations of the ACE language (e.g., restrictions on modal verbs, conditional clauses, temporality, and anaphora) while preserving the normative intent of the original text. Ethical issues related to the evaluation of digital techniques applied to the legal domain are beyond the scope of this manuscript. Detailed assessments on these topics are presented in [17], [18].

Through a systematic analysis of the errors produced by the parser and the resolution strategies applied, this study proposes a reusable library of linguistic patterns designed to support future projects involving formalizing legal norms or case law in a controlled language. In the long term, such a methodology can foster the development of tools for automated reasoning, compliance checking, and semantic interoperability between legal systems, contributing to a vision of artificial intelligence in law as a collaborative, transparent, and interpretable technology.

The remainder of the paper is organized as follows: Section II discusses related work; Section III presents the characteristics of the ACE language; Section IV describes the proposed methodology; Section V reports the experimental results; Section VI addresses critical issues and implications; Section VII concludes the paper; and the Acknowledgements section follows.
<figure><img src="image_3.png" /><figcaption>Fig. 1. Overview of the proposed approach</figcaption></figure>

## II. RELATED WORK

In recent years, *Natural Language Processing* (NLP) has found widespread application in the legal domain, with the aim of supporting complex tasks such as legal text analysis, legal document generation, information retrieval, and even automated legal reasoning. Integrating NLP techniques and *Large Language Models* (LLMs) in the legal field has opened new perspectives for the representation and querying of legal knowledge, fostering the development of intelligent tools for legal assistance, document classification, outcome prediction, and semantic formalization. A comprehensive study on models applied to legal NLP was conducted in [10]. The authors examined legal datasets used for tasks such as binary and multilabel classification, multiple-choice question answering, summarization, and information retrieval. In parallel, [26] introduced a virtual assistant based on a modified version of the GPT-3 model, trained explicitly for legal contexts. In [13], the authors proposed a methodology to specialize LLMs for specific domains such as law. They developed Lawyer LLaMA, an LLM focused on legal applications enriched with legal knowledge through targeted fine-tuning and integrating a retrieval module to select relevant articles before generating responses. Similarly, [23] presented LexGPT, a language model based on the GPT-J architecture, further optimized using human feedback and professional legal data. The study presented in [15] proposes a predictive approach to determining the existence of the crime of road homicide based on the digital processing of documents describing crime scenes. In particular, NLP techniques and LLMs are used to extract relevant features from the natural language in the documents, which are then processed by a Boolean function to estimate the configurability of the offense. Some of the same authors, in [11], present a hardware-oriented variant of this approach. The migration of software functionalities to hardware for reasons of performance, reliability, and security is extensively documented in the literature [2]–[4], [9], [12], [22]. Alongside approaches based on large language models and advanced NLP techniques, another line of research has focused on developing *Controlled Natural Languages* (CNLs) to reduce

natural language ambiguity and improve knowledge's computational representation. Unlike LLMs, which learn linguistic patterns from large corpora, CNLs adopt a strategy guided by syntactic and semantic constraints, offering an interface that is interpretable by both humans and machines. For example, [28] provides an overview of the main controlled languages used for high-level knowledge representation, emphasizing their ability to balance the expressiveness of natural language with the precision of formal systems. A particularly relevant case is ACE, a well-defined subset of standard English that enables automatic and unambiguous translation into first-order logic. In [14], the authors describe ACE as a syntax that is readable by humans yet formally rigorous, serving as a bridge between natural language and formal systems. The APE translates ACE texts into DRSs, which can then be converted into OWL or RuleML [6] to enable formal and computable representation of legal knowledge, allowing for automatic inference and semantic interoperability. Moreover, ACE has been used in various application contexts, including software requirements specification and ontology verbalization. In [14], the authors demonstrate how ACE can be employed to write formal specifications that are readable both by computer scientists and non-technical users. Similarly, [19] shows how ACE can be used to translate formal ontologies into a comprehensible language, and vice versa. Despite their advantages in terms of readability and formalizability, CNLs also present some limitations. In [21], the author critically analyzes the usability and expressiveness of CNLs, highlighting the need for a trade-off between linguistic flexibility and logical rigor. In [16], the author explores the extension of controlled languages to include forms of non-monotonic reasoning (default reasoning), emphasizing the necessity of handling complex normative inferences in knowledge representation systems. For these reasons, human involvement proves to be essential in the formalization process. In this context, the human-in-the-loop approach—thanks to the integration of human input—allows for mediation between the syntactic precision required by controlled languages and the complexity of the reasoning expressed in legal texts, particularly in domains such as law.
### III. BACKGROUND: ACE & APE

ACE is a controlled natural language developed with the goal of bridging the gap between human language and formal logical representations. It is a well-defined subset of natural English, designed to be simultaneously understandable by human users and formally interpretable by computational systems. Every sentence written in ACE adheres to strict grammar. When a sentence complies with ACE syntax, the APE automatically generates the corresponding semantic structure, known as a DRS [14]. One of the distinguishing features of ACE is its controlled ambiguity: sentences are constructed in such a way as to eliminate interpretative ambiguity, making it particularly well-suited for contexts that demand semantic precision—such as software requirements specification, ontology modeling, and more recently, legal formalization [14]. Unlike other formal languages, ACE does not require users to have knowledge of symbolic logic, offering a syntax that remains close to natural English while still enforcing strict syntactic and lexical constraints [14]. To prevent grammatical and semantic ambiguity, the APE parser requires, in some instances, the explicit declaration of a word's grammatical category using disambiguation prefixes. This is necessary when a word can play multiple roles (e.g., noun, verb, adjective) and the parser cannot determine its correct syntactic function. A prefix indicating the lexical category must be prepended to the term in such situations. The main categories are as follows:

* n: for nouns (e.g., n:vehicle, n:person)

* v: for verbs (e.g., v:drive, v:own)

* a: for adjectives (e.g., a:insured, a:empty)

* p: for prepositions (e.g., p:in, p:before)

These prefixes, known as lexical categories, are essential for guiding the parser during syntactic analysis, allowing for the correct interpretation of each term within a sentence. This mechanism is crucial to ensure an unambiguous translation into first-order logic and to guarantee semantic interoperability between different systems. ACE supports common constructs of normative language such as conditional propositions ("if – then"), universal expressions ("every", "each"), existential quantifiers ("there is a"), as well as temporal and causal relations. However, to ensure compatibility with APE, it imposes restrictions on using anaphora, modal verbs, implicit negations, and coordinated structures that are not explicitly resolved. This leads to a guided reformulation process—often iterative—that can be effectively managed in human-in-the-loop settings, where the legal expert or human operator collaborates with the system to ensure syntactic correctness and semantic coherence. In recent years, ACE has also been adopted for ontology verbalization (e.g., OWL) and in contexts related to artificial intelligence and law, opening new opportunities for the development of legal interfaces that are both human-readable and machine-interpretable [1]. Its use in formalizing legal norms and judicial decisions fits into a broader research direction to make the law machine-processable without compromising transparency or human oversight. In summary, controlled languages like ACE represent promising tools for

building intelligible interfaces between humans and machines, capable of combining natural readability with computational precision. The evolution of these languages is now moving toward greater semantic flexibility, integration with legal ontologies, and adaptability to specific scenarios such as legal formalization.

<figure><img src="image_4.png" /><figcaption>Fig. 2. The workflow of the proposed methodology</figcaption></figure>

### IV. THE METHODOLOGY

The methodology adopted in this study follows a human-in-the-loop approach inspired by the work presented in [25], and adapted to the context of legal formalization in ACE. The objective is twofold: (1) to produce machine-computable and semantically accurate representations of the articles of Directive 2009/103/EC; (2) to formalize in ACE the fundamental legal principles emerging from the case law of the Court of Justice of the European Union (specifically, the Vnuk case C-162/13 and Linea Directa Aseguradora C-100/21). We selected this directive as the first use case and these two jurisprudential cases as the second, as all of them are originally written in English. This choice, as demonstrated in this manuscript, ensures full compatibility with ACE. Working with legally binding texts already written in English minimizes the risk of translation bias and enables a more accurate and efficient formalization process.

Each of the two use cases we referred, was structured through the same sequence of steps based on a *prompt chain* applied to LLMs and followed by the necessary semantic and formal validations. The workflow of the proposed methodology is shown in Figure 2. Its main phases are as follows:
* **ACE generation prompt request:** The normative text was provided to the GPT-4o model with the instruction to produce a reformulation in ACE, free from subjective interpretations.

* **Error analysis:** If the output of the initial prompt contained errors, the model was asked to generate a new version of the ACE sentence, taking into account both the previous reformulation and the errors identified by the APE parser, such as non-conforming structures or unsupported syntax.

* **Consistency check:** Once the sentence was syntactically correct—as confirmed by the APE’s automatic generation of the corresponding DRS—the process continued with a manual review by a legal expert. This human intervention ensured structural, lexical, and conceptual consistency between the original legal text and its ACE formalization. If semantic discrepancies or relevant content losses emerged, the model was prompted to reformulate the sentence again, considering the suggested corrections and the syntactic validation cycle was repeated.

## V. RESULTS AND EVALUATION

The application of the proposed methodology resulted in a structured corpus of 45 sentences in ACE, divided as follows:

* 27 sentences derived from Articles 3, 9, and 10 of Directive 2009/103/EC;

* 18 sentences representing the fundamental legal principles extracted from the rulings Vnuk (C-162/13) and Linea Directa Aseguradora (C-100/21) of the Court of Justice of the European Union.

The corpus was analyzed using three dimensions: formal correctness (compatibility with the APE parser), semantic fidelity to the legal sources, and the types of errors encountered during the translation and validation process.

### A. Formal and Syntactic Validation with APE

Each sentence was individually submitted to the APE parser to verify its syntactic correctness and full computability within the ACE language. Out of a total of 45 analyzed sentences, only 12 were formally correct on the first attempt—i.e., accepted by APE without any errors. The remaining 33 sentences generated at least one error during the parsing phase, amounting to a total of over 70 error messages. Each error was corrected iteratively through linguistic reformulations that were compliant with the ACE language’s syntactic and semantic rules. The most frequent errors concerned, first and foremost, the improper use of conditional constructions. In ACE, conditional syntax requires the explicit presence of the *if-then* structure; otherwise, the logical meaning of the sentence cannot be correctly interpreted. A typical example is shown in row (1) of Table I: the initial sentence *The compensation body acts if the liable person is unknown* was rejected by APE due to the absence of an explicit *then*. The correct reformulation the parser accepts is: *If the liable person is unknown, then the compensation body acts*. A second common error involved using ambiguous disjunctions, which

do not comply with ACE’s linear syntax. In row (2) of Table I, the sentence *The injury happened in a public or private place* triggered a parsing error because ACE cannot correctly interpret the disjunction between adjectives. It was thus reformulated as: *The injury happened in a public place or in a private place*.

Several corrections were made to using terms or expressions unsupported by ACE’s controlled vocabulary. In row (3) of Table I, for example, the sentence *The vehicle is based in the territory of a member state* triggered an error because the parser does not recognize the expression *based in* as syntactically valid. The compatible reformulation was *The vehicle has a base in the territory of a member state*. Similarly, in row (4), the sentence *The rule has the same effect* was modified by replacing *same* with *equal*, while in row (5), both *such* and *must not* had to be removed, as they are not supported by the ACE language. Additional issues involved the expression of temporal references. As shown in row (6) of Table I, ACE does not accept dates in natural language format; therefore, the sentence *The transitional period can exist until 11 June 2012* was reformulated as *The transitional period can exist until 20120611*.

### B. Semantic Validation by Legal Experts

The 45 formally validated sentences were subsequently subjected to a semantic review by two experts in European law, both with extensive experience in analyzing EU legislation and case law from the Court of Justice. The aim of this second phase was to assess whether the ACE formulations, despite their syntactic transformation, retained a meaning entirely consistent with the original legal texts from which they were derived.

Overall, the validation confirmed the legal accuracy of the ACE sentences. However, some recurring issues emerged that, while not compromising semantic correctness, highlight essential aspects from an interpretative and stylistic standpoint.

One issue concerns terminological generality observed in some sentences. In particular, the expression *The measures must ensure something*, although syntactically correct, was deemed semantically weak compared to the original legal formulation, which clearly specified the object of the obligation (*to ensure that civil liability [...] is covered by insurance*). This case is illustrated in row (7) of Table I. According to the experts, the generality of the ACE expression does not hinder understanding but reduces the semantic strength of the text compared to the normative original.

A second critical point concerned the absence of explicit logical links, typical of legal language, such as conditional or causal subordination. A relevant example is the ACE formulation *The compensation body acts. The liable person is unknown*, shown in row (8) of Table I. Although the causal connection is implied, experts noted that the lack of an explicit *if-then* structure makes the legal interpretation of the norm less immediate.

Another issue involves the risk of terminological ambiguity, particularly with terms that have a precise technical meaning in
<table><caption>TABLE I
SENTENCES REFORMULATED IN ACE WITH INDICATION OF THE TYPE OF ERROR (C: COMPATIBILITY; S: SYNTACTIC)</caption><thead><tr><th>No.</th><th>Original sentence initially given as input</th><th>Identified issue</th><th>Corrected ACE sentence or note for correction</th><th>C</th><th>S</th></tr></thead><tbody><tr><td>1</td><td>The compensation body acts if the liable person is unknown.</td><td>Missing <strong>then</strong> in the conditional structure.</td><td>If the liable person is unknown, <strong>then</strong> the compensation body acts.</td><td>✓</td><td>✓</td></tr><tr><td>2</td><td>The injury happened in a public or private place.</td><td>Ambiguous disjunction.</td><td>The injury happened in a public place or in a private place.</td><td>✓</td><td></td></tr><tr><td>3</td><td>The vehicle is <strong>based in</strong> the territory of a member state.</td><td><strong>Based in</strong> not recognized by ACE.</td><td>The vehicle has a base in the territory of a member state.</td><td>✓</td><td></td></tr><tr><td>4</td><td>The rule has the <strong>same</strong> effect in all member states.</td><td>Use of <strong>same</strong> not supported.</td><td>The rule has an equal effect in all member states.</td><td>✓</td><td></td></tr><tr><td>5</td><td><strong>Such measures must not</strong> be discriminatory.</td><td>Use of <strong>such</strong> and <strong>must not</strong>.</td><td>The measures are required. The measures are not discriminatory.</td><td>✓</td><td></td></tr><tr><td>6</td><td>The transitional period can exist until 11 June 2012.</td><td>Invalid date format.</td><td>The transitional period can exist until 20120611.</td><td>✓</td><td></td></tr><tr><td>7</td><td>The measures must ensure something.</td><td>Generic and uninformative formulation.</td><td>The measures must ensure that civil liability is covered by insurance.</td><td></td><td>✓</td></tr><tr><td>8</td><td>The compensation body acts. The liable person is unknown.</td><td>Missing explicit logical link.</td><td>If the liable person is unknown, <strong>then</strong> the compensation body acts.</td><td></td><td>✓</td></tr><tr><td>9</td><td>The vehicle has a base in the territory. The base is <strong>normal</strong>.</td><td>Semantic ambiguity of the term <strong>normal</strong>.</td><td>The vehicle has a <strong>normal</strong> base in the territory of a member state.</td><td></td><td>✓</td></tr><tr><td>10</td><td>A national of a member-state can suffer an injury. [...] does not have a responsible national insurers bureau.</td><td>Excessive fragmentation.</td><td>Reformulate using conditional structure.</td><td></td><td>✓</td></tr><tr><td>11</td><td>A rule exists. The rule has exceptions.</td><td>Missing legal subject.</td><td>A member state establishes a rule. The rule has exceptions.</td><td></td><td>✓</td></tr></tbody></table>

European law. For example, in the ACE sentence *The vehicle has a base in the territory. The base is normal* (row 9 of Table I), and the term *normal* is used according to ACE syntax to indicate that the vehicle is usually stationed in a given Member State. However, the experts observed that, outside its technical legal context, the adjective *normal* may appear vague or ambiguous, lacking an explicit reference to the legal notion of *normal base* as defined in the directive.

Another observation concerned the fragmentation of argumentative structure: the breakdown of complex provisions into atomic sentences results in a loss of rhetorical unity of the norm. This emerged, for instance, in the representation of the concept of insurance coverage during a direct journey between two Member States, as shown in row (10) of Table I: the series of ACE sentences *A national of a member-state can suffer an injury. The injury happens during a direct journey. The journey crosses a crossed territory. The crossed territory does not have a responsible national insurers bureau* accurately reproduces the individual normative elements but separates the conditional structure and the legal context in which it operates, requiring the reader to reconstruct the overall meaning.

Finally, the absence of explicit legal subjects was identified in some sentences. In row (11) of Table I, the formulation *A rule exists. The rule has exceptions* does not clarify who establishes the rule or which subject is bound by it. This lack of attribution can generate ambiguity in interpretation, especially when the legal subject (e.g., the Member State, the competent authority) is an integral part of the norm.

Despite these remarks, both experts confirmed that all the ACE sentences are semantically consistent with the corresponding legal provisions. The identified issues do not concern logical or legal obtaining computable and semantically accurate representations, which validated me as particularly relevant when aiming to ensure effective interaction between computational systems and legal operators. The validation thus confirmed the reliability of the proposed methodology, reinforcing the potential of ACE as a tool for the formalization of norms and the automated processing of law.

## VI. DISCUSSION

The experiment of translating legal and case law texts into ACE highlighted both the potential and the current limitations of the *human-in-the-loop* approach applied to the compatibilization of law. On the one hand, the ability to obtain com-

putable and semantically accurate representations—validated both by the APE parser and legal experts—confirms the validity of using ACE as a bridge between natural language and automated legal reasoning systems. On the other hand, the translation process showed that applying ACE to legal texts requires significant efforts in rewriting, conceptual mediation, and linguistic adaptation. The high number of initial errors (over 70 across 45 sentences) suggests that, despite the apparent simplicity of the language, ACE is not immediately compatible with the syntactic and semantic structure of positive law. Common expressions in legal practice (such as implicit conditional statements, negative modal verbs, or temporal formulas) must be artificially reformulated to be accepted by the parser. This entails a risk of losing interpretive nuances, which must be constantly mitigated through human validation. However, as the prompting iterations continued, the number and severity of errors gradually decreased. This improvement was likely due to the LLM adapting through the chained prompting strategy, learning from previous corrections, and leveraging contextual patterns. Consequently, the newly generated ACE sentences increasingly aligned with the parser's expectations, reducing the need for further revision. The role of the legal interpreter thus proves crucial in every phase of the process, from selecting textual fragments to rewriting, from semantic verification to identifying recurring patterns. Collaboration between legal experts and computational tools emerges as a necessary condition to overcome the so-called “bottleneck” in the representation of normative knowledge. Moreover, structuring the process into prompt chains, inspired by recent interaction methodologies with language models, proved particularly effective in maintaining coherence between the source text, the controlled formulation, and its legal interpretation. The resulting ACE corpus is not only formally valid but can also be effectively used in experiments on automated reasoning, normative simulation, or consistency verification across legal provisions. Ultimately, this study shows that the formalization of law in controlled language is not merely a technical translation task but rather a structured interpretative exercise that demands a constant balance between syntactic rigor and conceptual fidelity. In this respect, ACE can become both a didactic and operational tool for exploring new forms of legal representation in the context of artificial intelligence.
## VII. CONCLUSION

This study proposed and validated a *human-in-the-loop* methodology for the computable formalization of legal and case law texts in a controlled language, using ACE as an interface between legal norms and automated reasoning systems. The concrete application of the methodology to *Directive 2009/103/EC* and the rulings of the Court of Justice of the European Union (*Vnuk and Linea Directa Aseguradora*) led to the creation of an ACE corpus composed of 45 sentences formally and semantically validated. The results show that, although an iterative and guided process is required to ensure syntactic correctness, ACE is well-suited for representing legal norms in a structured, precise, and machine-interpretable way. The integration of LLMs, the APE parser, and legal expertise made it possible to overcome many of the challenges posed by formal syntax while preserving the original normative meaning. This work contributes in two key ways: first, it offers a systematic approach for transforming legal texts into computable language applicable to legal AI, compliance checking, and normative modeling. Second, it provides a validated ACE corpus that can serve as a foundation for future experiments in semantic-legal research and the development of interoperable legal ontologies. Future directions include extending the methodology to other directives and legal sources at the European level, exploring the integration of ACE with deontic and temporal logics, and embedding the corpus into legal reasoning and simulation tools. Furthermore, using controlled languages such as ACE could open new avenues in legal education, constraint-based legislative drafting, and semantic interoperability among automated legal systems.

## VIII. ACKNOWLEDGEMENTS

We would like to thank Aurelia Tamò-Larrieux (University of Lausanne) and Clement Guitton (University of St Gallen), who first introduced us to the topic of legal formalization through digital-based methodologies, particularly the use of ACE and APE. Their insights were valuable in shaping the initial direction of this research.

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