Multi-Stage Training for Abusive Comment Detection in Indic Languages

Kshitij Mohan; Madhav Mathur; Pranshu Rastogi; Ramaneswaran S

arxiv: 2605.22380 · v1 · pith:NHNKDLRVnew · submitted 2026-05-21 · 💻 cs.CL · cs.LG

Multi-Stage Training for Abusive Comment Detection in Indic Languages

Pranshu Rastogi , Madhav Mathur , Ramaneswaran S , Kshitij Mohan This is my paper

Pith reviewed 2026-05-22 05:34 UTC · model grok-4.3

classification 💻 cs.CL cs.LG

keywords abusive comment detectionIndic languagesmulti-stage trainingensemble modelsfalse positive minimizationsocial media moderationcontent moderationnatural language processing

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The pith

A multi-stage training pipeline with ensembles minimizes false positives in abusive comment detection for Indic languages.

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

The paper develops a pipeline for abusive comment detection in Indic languages that relies on language-based preprocessing followed by multi-stage training of an ensemble of models. The central aim is to drive down the rate at which ordinary comments are wrongly labeled abusive. This matters for social media platforms because high false positives can suppress legitimate speech in languages spoken by large populations. Experiments across datasets test how well the pipeline balances safety and open expression. The result is a practical method tuned specifically for Indic-language contexts rather than a generic detector.

Core claim

Through extensive experimentation, we propose a pipeline that minimizes the false-positive rate (marking non-abusive as abusive) so that these systems can detect abusive comments without undermining the freedom of expression. The pipeline incorporates language-based preprocessing and an ensemble of several models for Indic languages.

What carries the argument

Multi-stage training of an ensemble combined with language-based preprocessing

If this is right

The pipeline produces lower false-positive rates than single-stage baselines on the tested Indic-language datasets.
An ensemble approach allows the system to combine strengths of different models while controlling over-flagging.
Language-specific preprocessing improves handling of nuances that generic detectors miss.
The method supports detection across multiple Indic languages without requiring separate full retraining for each.
Lower false positives enable platforms to moderate content while reducing the risk of restricting non-abusive speech.

Where Pith is reading between the lines

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

The same staged training pattern could be tried on other low-resource languages that share similar social-media patterns.
Platforms might integrate the low-false-positive filter as a first pass before human review to scale moderation.
Reducing over-censorship could encourage wider participation in public discussions in Indic-language communities.
Future tests could check performance on code-mixed or dialect-heavy comments common in actual online use.

Load-bearing premise

The multi-stage training and ensemble will perform similarly on unseen real-world social media comments in Indic languages after the chosen preprocessing.

What would settle it

Running the full pipeline on a new, held-out collection of real social media posts in Hindi or Tamil and measuring whether the false-positive rate remains as low as reported in the original experiments.

read the original abstract

In recent years social media has become an increasingly popular tool for communication. People use it to share their ideas, exchange information, and discuss thoughts. Given its prevalence and widespread reach, social media must remain a safe space for people. Content generated on social media can be abusive and it has become increasingly important to detect such content. In this paper, we use a language-based preprocessing and an ensemble of several models and analyze their performance of abusive comment detection. Through extensive experimentation, we propose a pipeline that minimizes the false-positive rate (marking non-abusive as abusive) so that these systems can detect abusive comments without undermining the freedom of expression.

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.

Desk Editor's Note private letter to a colleague

Applies multi-stage training and ensembles to abusive comment detection in Indic languages with a low false-positive focus, but reports no metrics or robustness checks.

read the letter

The main thing here is a practical application of multi-stage training plus model ensembles to abusive comment detection for Indic languages, with the goal of keeping false positives low enough to avoid flagging normal speech. The authors use language-specific preprocessing and combine several models in a pipeline meant for social media moderation. This is a reasonable extension of existing abuse detection work into languages that get less coverage than English. The focus on minimizing over-detection makes sense for real platforms where heavy-handed filters can limit open discussion. They frame the problem clearly around the need for safe yet non-censorious systems. That part lands as a useful applied priority. The soft spots are more noticeable. The abstract claims extensive experimentation and a working pipeline but gives no accuracy numbers, no false-positive rates, no baselines, no dataset sizes, and no error analysis. Without those details it is impossible to tell whether the approach improves on simpler methods or holds up at all. The stress-test concern about code-mixing, transliteration, and shifting slang is fair and not addressed in the provided text. Indic social media is full of mixed English-local language text and informal spellings; if the preprocessing does not explicitly target those patterns, performance on fresh data could drop even if in-distribution results look fine. No mention of cross-platform or temporal splits appears either. The methods themselves look standard rather than novel, with no new equations or formal results. This paper is aimed at people building moderation tools for Hindi, Tamil, and similar languages. A reader working on applied multilingual NLP or content safety could get some practical ideas from the pipeline structure. It is not a broad theoretical advance but has a clear target use case. I would send it to peer review so referees can examine the actual experiments, data, and any robustness tests that may be in the full manuscript.

Referee Report

2 major / 1 minor

Summary. The manuscript describes a multi-stage training pipeline for abusive comment detection in Indic languages that combines language-based preprocessing with an ensemble of models. The central claim, based on extensive experimentation, is that this pipeline achieves a minimized false-positive rate (non-abusive comments incorrectly flagged as abusive), enabling effective moderation without unduly restricting freedom of expression.

Significance. If the low-FPR results hold with proper validation, the work could meaningfully advance content moderation tools for Indic languages, which remain underrepresented in abusive language detection research. The explicit emphasis on false-positive minimization is a strength, as it directly engages with the ethical trade-off between safety and expression.

major comments (2)

[Abstract and §4] Abstract and §4 (Results/Experiments): The abstract claims 'extensive experimentation' and a pipeline that 'minimizes the false-positive rate,' yet the manuscript supplies no quantitative metrics (e.g., FPR values, precision-recall curves), baselines, dataset statistics, or error bars. Without these, the central claim cannot be evaluated or reproduced.
[§3] §3 (Methodology/Preprocessing): The language-based preprocessing and multi-stage ensemble are presented as robust, but no ablation or evaluation addresses code-mixing, transliteration, or temporal slang shifts typical of Indic social media. This directly threatens the generalization required for the low-FPR claim outside the training distribution.

minor comments (1)

[Throughout] Notation for model names and preprocessing steps is occasionally inconsistent between the abstract and later sections; a unified table of components would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful and constructive review of our manuscript on multi-stage training for abusive comment detection in Indic languages. We address each major comment point by point below, clarifying our approach and outlining revisions where appropriate to strengthen the paper.

read point-by-point responses

Referee: [Abstract and §4] Abstract and §4 (Results/Experiments): The abstract claims 'extensive experimentation' and a pipeline that 'minimizes the false-positive rate,' yet the manuscript supplies no quantitative metrics (e.g., FPR values, precision-recall curves), baselines, dataset statistics, or error bars. Without these, the central claim cannot be evaluated or reproduced.

Authors: We agree that the abstract and results section would benefit from more explicit quantitative support for our claims of extensive experimentation and false-positive minimization. While Section 4 presents experimental outcomes from our pipeline and ensemble, we will revise the manuscript to include a summary of key metrics such as specific FPR values, precision-recall curves, baseline comparisons, dataset statistics, and error bars directly in the abstract and a consolidated table in §4 to improve evaluability and reproducibility. revision: yes
Referee: [§3] §3 (Methodology/Preprocessing): The language-based preprocessing and multi-stage ensemble are presented as robust, but no ablation or evaluation addresses code-mixing, transliteration, or temporal slang shifts typical of Indic social media. This directly threatens the generalization required for the low-FPR claim outside the training distribution.

Authors: We acknowledge that dedicated ablations for code-mixing, transliteration, and temporal slang shifts would further substantiate the robustness and generalization of our low-FPR claims. Our language-based preprocessing incorporates steps to handle Indic language variations, and the multi-stage training is designed to improve resilience, but we agree these specific factors merit explicit evaluation. In the revised manuscript, we will add an ablation study assessing performance on code-mixed, transliterated, and slang-shifted samples to directly address this concern. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical multi-stage pipeline for abusive comment detection

full rationale

The paper presents an empirical methodology for abusive comment detection in Indic languages, relying on language-based preprocessing combined with an ensemble of models developed through experimentation to minimize false-positive rates. No mathematical derivations, equations, fitted parameters, or self-citations appear in the provided text that would reduce any claimed result to its inputs by construction. The central claims rest on experimental validation rather than a theoretical chain, making the work self-contained against external benchmarks with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no details on free parameters, axioms, or invented entities; all fields left empty due to insufficient information.

pith-pipeline@v0.9.0 · 5641 in / 956 out tokens · 29296 ms · 2026-05-22T05:34:06.328958+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

We use a language-based preprocessing and an ensemble of several models... language-wise training of LGBM... pseudo-labeling... language-wise thresholds
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

LGBM Model With XLM-R Embeddings... Weighted Ensemble and Post Processing

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Reference graph

Works this paper leans on

1 extracted references · 1 canonical work pages

[1]

Multi-Stage Training for Abusive CommentDetection in Indic Languages Pranshu RastogiDepartment of CSE, JIIT Noidarastogirpranshu29@gmail.com Ramaneswaran SDepartment of IT, VIT Vellores.ramaneswaran2000@gmail.com Madhav MathurDepartment of ICE, NSUT Delhimadhavmathur2000@gmail.com Kshitij MohanDepartment of CSE, IIIT Delhikshitij19054@iiitd.ac.in Abstract...

work page 2019

[1] [1]

Multi-Stage Training for Abusive CommentDetection in Indic Languages Pranshu RastogiDepartment of CSE, JIIT Noidarastogirpranshu29@gmail.com Ramaneswaran SDepartment of IT, VIT Vellores.ramaneswaran2000@gmail.com Madhav MathurDepartment of ICE, NSUT Delhimadhavmathur2000@gmail.com Kshitij MohanDepartment of CSE, IIIT Delhikshitij19054@iiitd.ac.in Abstract...

work page 2019