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arxiv: 2604.12442 · v1 · submitted 2026-04-14 · 💻 cs.CL

GLeMM: A large-scale multilingual dataset for morphological research

Hathout Nabil (CLLE , Comue de Toulouse) , Basilio Calderone (CLLE , UBM) , Fiammetta Namer (ATILF , UL) , Franck Sajous (CLLE-ERSS This is my paper

Pith reviewed 2026-05-10 15:06 UTC · model grok-4.3

classification 💻 cs.CL
keywords derivational morphologymultilingual datasetWiktionaryword formationmorphological annotationEuropean languagescomputational morphology
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The pith

GLeMM supplies a large automated multilingual dataset of derivational morphology drawn from Wiktionary to support data-driven analysis of word formation across languages.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents GLeMM as a new resource for investigating how form and meaning interact when new words are created. It is constructed through an identical automated process applied to Wiktionary for seven languages, with added annotations for morphological features and semantic descriptions on many entries. This scale and consistency allow studies of derivational morphology to move past small hand-selected examples toward replicable, generalizable results. A sympathetic reader would care because prior work on these questions has often relied on limited data or intuition, limiting what can be firmly established.

Core claim

GLeMM is a derivational resource of large size with coverage of seven European languages, a fully automated design that is identical across languages, automatic annotation of morphological features on each entry, and encoding of semantic descriptions for a significant subset. Created from Wiktionary articles, it enables researchers to address questions such as the role of form and meaning in word-formation and to develop and test computational methods that identify the structures of derivational morphology.

What carries the argument

The automated extraction and annotation pipeline applied identically to Wiktionary articles, which generates entries carrying morphological annotations and partial semantic descriptions.

If this is right

  • Researchers can now examine the role of form and meaning in word-formation with large-scale, consistent data instead of limited observations.
  • Computational methods for identifying derivational morphology structures can be developed and tested experimentally on the same resource.
  • Morphological studies can be replicated and generalized across German, English, Spanish, French, Italian, Polish, and Russian.
  • Data-driven description in morphology becomes feasible beyond intuition-based approaches.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The dataset could serve as training material for machine learning systems that process word formation in new texts.
  • Direct comparisons of annotated patterns across the seven languages might highlight both shared and language-specific tendencies in derivation.
  • Applying the same pipeline to additional languages would extend the scope for testing claims about universal aspects of morphology.

Load-bearing premise

The automated extraction and annotation pipeline applied identically to Wiktionary articles produces accurate and consistent morphological information across all seven languages without significant language-specific errors or coverage gaps.

What would settle it

A manual verification of randomly sampled entries from each of the seven languages that finds frequent inaccuracies or inconsistencies in the morphological annotations would show the resource cannot support reliable research.

read the original abstract

In derivational morphology, what mechanisms govern the variation in form-meaning relations between words? The answers to this type of questions are typically based on intuition and on observations drawn from limited data, even when a wide range of languages is considered. Many of these studies are difficult to replicate and generalize. To address this issue, we present GLeMM, a new derivational resource designed for experimentation and data-driven description in morphology. GLeMM is characterized by (i) its large size, (ii) its extensive coverage (currently amounting to seven European languages, i.e., German, English, Spanish, French, Italian, Polish, Russian, (iii) its fully automated design, identical across all languages, (iv) the automatic annotation of morphological features on each entry, as well as (v) the encoding of semantic descriptions for a significant subset of these entries. It enables researchers to address difficult questions, such as the role of form and meaning in word-formation, and to develop and experimentally test computational methods that identify the structures of derivational morphology. The article describes how GLeMM is created using Wiktionary articles and presents various case studies illustrating possible applications of the resource.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The manuscript presents GLeMM, a large-scale multilingual dataset for derivational morphology covering seven European languages (German, English, Spanish, French, Italian, Polish, Russian). It is constructed via a fully automated, identical pipeline from Wiktionary articles, with automatic annotation of morphological features on each entry and semantic descriptions for a significant subset. The paper describes the creation process and includes case studies to illustrate applications for studying form-meaning relations in word-formation and for developing/testing computational methods in derivational morphology.

Significance. If the automated pipeline produces accurate and consistent annotations, GLeMM would be a valuable resource: its scale, uniform cross-lingual design, and coverage of derivational data address a gap in replicable, data-driven morphological research. The automated construction and inclusion of semantic encodings are strengths that could support large-scale experiments on form-meaning mappings and method evaluation.

major comments (3)
  1. [§3 (Construction Pipeline)] Construction section (described in the abstract and §3): The central claim that the identical automated extraction and annotation pipeline yields reliable morphological information across all seven languages lacks any quantitative validation. No precision, recall, error rates, coverage statistics, or gold-standard comparisons are reported, despite acknowledged differences in Wiktionary article structure and quality by language. This directly undermines the weakest assumption that the resource reliably supports the stated research questions.
  2. [§4 (Annotations)] Annotation and semantic encoding (abstract and §4): While morphological features are automatically annotated and semantic descriptions are provided for a subset, the paper provides no details on validation of these annotations (e.g., inter-annotator agreement or per-language accuracy metrics). This is load-bearing for claims about enabling form-meaning analyses.
  3. [§6 (Case Studies)] Case studies (§6): The applications for addressing questions on word-formation and testing computational methods are illustrated but without any empirical assessment of dataset quality or utility in those tasks, such as baseline experiments or error analysis on the extracted data.
minor comments (3)
  1. [Introduction] The abstract and introduction would benefit from explicit comparison to existing morphological resources (e.g., UniMorph or derivational databases) to clarify novelty in scale and automation.
  2. [§4] Notation for morphological features and semantic encodings should be defined more clearly, perhaps with an example table entry for each language.
  3. [§6] The paper mentions 'various case studies' but the text would be improved by a summary table linking each study to specific dataset properties used.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback and positive evaluation of GLeMM's potential significance. We address each major comment below and describe the revisions we will make to the manuscript.

read point-by-point responses

  1. Referee: [§3 (Construction Pipeline)] Construction section (described in the abstract and §3): The central claim that the identical automated extraction and annotation pipeline yields reliable morphological information across all seven languages lacks any quantitative validation. No precision, recall, error rates, coverage statistics, or gold-standard comparisons are reported, despite acknowledged differences in Wiktionary article structure and quality by language. This directly undermines the weakest assumption that the resource reliably supports the stated research questions.

    Authors: We agree that quantitative validation metrics would strengthen the presentation of the pipeline's reliability. The manuscript's primary focus is on documenting the uniform, fully automated construction process that enables cross-lingual comparability. In the revision, we will add to §3 coverage statistics (entries and features per language), precision/recall estimates from manual inspection of random samples (200 entries per language), and a discussion of how the pipeline handles Wiktionary structural differences. These additions will provide concrete support for the resource's usability. revision: yes

  2. Referee: [§4 (Annotations)] Annotation and semantic encoding (abstract and §4): While morphological features are automatically annotated and semantic descriptions are provided for a subset, the paper provides no details on validation of these annotations (e.g., inter-annotator agreement or per-language accuracy metrics). This is load-bearing for claims about enabling form-meaning analyses.

    Authors: We acknowledge that the current version lacks explicit validation details for the automatic annotations. We will expand §4 to describe the annotation heuristics and rules in greater detail, report per-language accuracy figures obtained by comparing a held-out sample against manual gold standards, and clarify the extraction and coverage of semantic descriptions. This will better substantiate the dataset's value for form-meaning research. revision: yes

  3. Referee: [§6 (Case Studies)] Case studies (§6): The applications for addressing questions on word-formation and testing computational methods are illustrated but without any empirical assessment of dataset quality or utility in those tasks, such as baseline experiments or error analysis on the extracted data.

    Authors: The case studies are illustrative of potential uses rather than exhaustive evaluations. We agree that adding empirical elements would improve the section. In the revision, we will incorporate a baseline experiment in one case study (e.g., a rule-based derivational relation identifier evaluated on GLeMM data) together with performance metrics and error analysis. This will demonstrate practical utility while remaining within the scope of a dataset paper. revision: yes

Circularity Check

0 steps flagged

No circularity: dataset construction is self-contained

full rationale

The paper describes the automated extraction of GLeMM from Wiktionary articles via a uniform pipeline, with automatic morphological annotation and partial semantic encoding. No equations, fitted parameters, predictions, or derivations appear in the abstract or described content. Claims about enabling research on form-meaning relations follow directly from the stated size, coverage, and identical processing across languages, without any reduction to self-defined quantities or load-bearing self-citations. This matches the expected non-circular outcome for a resource paper.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the quality and consistency of Wiktionary source data plus the correctness of the automated extraction pipeline; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Wiktionary articles contain sufficiently accurate and structured morphological information that can be extracted automatically and uniformly across languages
    The entire resource is built from Wiktionary using an identical automated design.

pith-pipeline@v0.9.0 · 5544 in / 1220 out tokens · 34335 ms · 2026-05-10T15:06:55.946022+00:00 · methodology

discussion (0)

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