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arxiv: 2606.30914 · v1 · pith:UVF7AF2Jnew · submitted 2026-06-29 · 💻 cs.CL

Beyond Clean Text: Evaluating Encoder and Decoder Robustness for Bangla Event Detection in Noisy Text

Tanvir Ahmed Sijan , S. M Golam Rifat , Nayeemul Islam , Md. Musfique Anwar This is my paper

Pith reviewed 2026-07-01 01:43 UTC · model grok-4.3

classification 💻 cs.CL
keywords Banglaevent detectionnoisy textencoder modelsdecoder LLMsrobustnessASR transcripts
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The pith

Encoder models for Bangla event detection lose more performance under noise than decoder-only LLMs.

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

This paper creates a new benchmark for detecting events in Bangla news text that includes both clean and noisy versions. It compares encoder models, which are fine-tuned on the data, against decoder-only large language models that are instruction-tuned. The results show encoders start stronger on clean text but suffer larger drops when the text has ASR errors or spelling corruption, while the decoder models hold up better especially when the key event words are messed up. This matters for building systems that work on real-world messy text like transcripts rather than perfect articles. The work also tests mixing clean and noisy data during training and finds it helps encoders more.

Core claim

Encoder models achieve higher performance on clean text but degrade substantially under noise, whereas decoder-only LLMs are markedly more robust, particularly when event triggers are corrupted.

What carries the argument

The generalized Bangla news event ontology and 9,979-sentence benchmark with clean, ASR, and orthographically corrupted conditions, used to evaluate fine-tuned encoders against instruction-tuned decoders.

If this is right

  • Combined training on clean and noisy data serves as an effective regularization strategy that narrows the robustness gap for encoder architectures.
  • Model scaling consistently improves the robustness of decoder-only LLMs.
  • Embedding annotation guidelines during instruction tuning establishes a higher performance baseline on noisy text.

Where Pith is reading between the lines

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

  • The robustness pattern may apply to other low-resource languages facing similar text noise issues.
  • Systems for real-time event detection from speech might prefer decoder models due to their noise tolerance.

Load-bearing premise

The 9,979-sentence benchmark with its three noise conditions sufficiently represents real-world Bangla text noise and event distributions for the robustness conclusions to generalize.

What would settle it

Running the same models on a separate Bangla event detection dataset collected from different sources with new noise patterns and finding that encoder models no longer degrade more than decoders.

Figures

Figures reproduced from arXiv: 2606.30914 by Md. Musfique Anwar, Nayeemul Islam, S. M Golam Rifat, Tanvir Ahmed Sijan.

Figure 1
Figure 1. Figure 1: Real-world noise substantially degrades event detection, even when the event trigger itself re￾mains unchanged. Clean examples are drawn from our Clean Test set, while their counterparts are generated using simulated orthographic noise. ASR examples are collected from real Bangla news video transcripts and independently annotated using the same event on￾tology, introducing naturally occurring transcription… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the dataset construction, training, and evaluation pipeline. We develop a generalized Bangla [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Frequency distribution of the 40 event types [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Decomposition of Macro-F1 performance across model architectures and training conditions into re [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of performance degradation between Generative LLMs (left) and Encoder architectures [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Pairwise McNemar’s significance tests at [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Overview of the ontology construction pro [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of event instances across the 40 event subtypes in the News and ASR corpora. Both corpora [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
read the original abstract

Event detection (ED) systems are typically evaluated on clean, curated text, leaving their robustness to real-world noise largely unexplored, particularly for low-resource languages such as Bangla. We introduce a generalized Bangla news event ontology and a benchmark comprising 9,979 annotated sentences across 40 event subtypes, spanning clean news text, real-world Automatic Speech Recognition (ASR) transcripts, and orthographically corrupted text. We systematically evaluate fine-tuned encoder-only models (BanglaBERT and XLM-R) alongside instruction-tuned decoder-only large language models (Llama 3 and Gemma 3). Our results reveal a clear architectural trade-off: encoder models achieve higher performance on clean text but degrade substantially under noise, whereas decoder-only LLMs are markedly more robust, particularly when event triggers are corrupted. We further show that embedding annotation guidelines during instruction tuning establishes a higher performance baseline on noisy text but yields inconsistent reductions in performance degradation across noisy conditions. Finally, model scaling consistently improves the robustness of decoder-only LLMs, while combined training on clean and noisy data serves as an effective regularization strategy that disproportionately benefits encoder architectures, significantly narrowing the robustness gap.

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

1 major / 2 minor

Summary. The paper introduces a Bangla event detection ontology and a 9,979-sentence benchmark spanning 40 subtypes across clean news text, real-world ASR transcripts, and orthographically corrupted text. It evaluates fine-tuned encoder-only models (BanglaBERT, XLM-R) against instruction-tuned decoder-only LLMs (Llama 3, Gemma 3), reporting that encoders achieve higher clean-text performance but degrade substantially under noise while decoders are more robust (especially to trigger corruption). Additional results show that embedding annotation guidelines during tuning raises noisy-text baselines, model scaling improves decoder robustness, and combined clean+noisy training narrows the robustness gap, particularly for encoders.

Significance. If the observed architectural trade-off and mitigation strategies hold beyond the specific benchmark, the work would provide actionable guidance for deploying event detection in noisy, low-resource settings such as ASR-derived or social-media Bangla text, while highlighting the value of decoder-only models for robustness.

major comments (1)
  1. [Benchmark construction section] Benchmark construction section: the claim that the three noise conditions (clean news, ASR transcripts, orthographic corruption) sufficiently proxy real-world Bangla noise distributions for generalizing the encoder/decoder robustness trade-off is not supported by any quantitative validation against external corpora (e.g., social-media text or outputs from additional ASR engines); without such checks the degradation patterns and the effectiveness of combined training could be artifacts of the chosen noise realizations.
minor comments (2)
  1. [Abstract and results sections] Abstract and results sections: the abstract states performance differences but the main text should explicitly report the precise metrics (F1, precision, recall), statistical tests, and per-condition breakdowns for each model to allow verification of the claimed trade-offs.
  2. [Noise conditions subsection] The paper should clarify whether the orthographic corruption mechanism is deterministic or stochastic and provide the exact corruption rate or distribution used, as this directly affects reproducibility of the trigger-corruption results.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the single major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Benchmark construction section] Benchmark construction section: the claim that the three noise conditions (clean news, ASR transcripts, orthographic corruption) sufficiently proxy real-world Bangla noise distributions for generalizing the encoder/decoder robustness trade-off is not supported by any quantitative validation against external corpora (e.g., social-media text or outputs from additional ASR engines); without such checks the degradation patterns and the effectiveness of combined training could be artifacts of the chosen noise realizations.

    Authors: We acknowledge the absence of quantitative validation against external corpora such as social-media text or outputs from additional ASR engines. The ASR transcripts are drawn from real-world Bangla ASR systems, and the orthographic corruption is modeled on attested error patterns in Bangla; however, we performed no distributional comparisons or statistical tests against broader external sources. In the revised version we will (1) qualify the benchmark-construction claims to describe the three conditions as representative proxies rather than validated proxies of all real-world Bangla noise, and (2) add an explicit limitations paragraph noting that observed degradation patterns and the benefits of combined training could be influenced by the specific noise realizations chosen. These changes will prevent over-generalization while retaining the benchmark’s utility for the evaluated settings. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical benchmark and model comparison

full rationale

The paper introduces a new Bangla event detection benchmark (9,979 sentences across clean, ASR, and orthographic conditions) and reports direct experimental results comparing encoder-only and decoder-only models. No derivations, equations, fitted parameters renamed as predictions, or self-citation chains appear in the abstract or described methodology. All claims (e.g., encoder degradation vs. decoder robustness) rest on observed performance metrics rather than any reduction to inputs by construction. This is standard empirical evaluation work with no load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical machine learning evaluation study. No mathematical derivations, free parameters, axioms, or invented entities are present.

pith-pipeline@v0.9.1-grok · 5749 in / 1057 out tokens · 52988 ms · 2026-07-01T01:43:16.301752+00:00 · methodology

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