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arxiv: 2604.04708 · v1 · submitted 2026-04-06 · 💻 cs.CL · cs.AI

BiST: A Gold Standard Bangla-English Bilingual Corpus for Sentence Structure and Tense Classification with Inter-Annotator Agreement

Abdullah Al Shafi , Swapnil Kundu Argha , M. A. Moyeen , Abdul Muntakim , Shoumik Barman Polok This is my paper

Pith reviewed 2026-05-10 19:18 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords bilingual corpusBangla Englishsentence structure classificationtense classificationinter-annotator agreementmultilingual NLPgrammatical annotationlow-resource languages
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The pith

BiST introduces a bilingual corpus of over 30,000 sentences labeled for syntactic structure and tense in Bangla and English.

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

The authors compile BiST from open encyclopedic and conversational sources, resulting in 30,534 sentences split between English and Bangla. They apply a multi-stage annotation process with three independent annotators to assign each sentence one of four structural types and one of three tenses. Agreement is measured separately for each dimension using Fleiss kappa, producing scores of 0.82 and 0.88. The resulting labels are shown to support stronger performance from dual-encoder models than from single multilingual encoders on the classification tasks. This setup supplies explicit grammatical supervision that can be used for controlled generation and cross-lingual modeling.

Core claim

The central claim is that BiST constitutes a gold-standard bilingual resource by combining 17,465 English and 13,069 Bangla sentences, each annotated for structure (Simple, Complex, Compound, Complex-Compound) and tense (Present, Past, Future), with dimension-wise Fleiss kappa agreement of 0.82 and 0.88 confirming label reliability. The corpus is constructed through preprocessing and automated language identification from representative sources, and baseline experiments establish that dual-encoder architectures using complementary language-specific representations outperform strong multilingual encoders on the two classification tasks.

What carries the argument

The BiST corpus itself, created via multi-stage annotation by three independent annotators and evaluated with dimension-wise Fleiss kappa for structural and temporal labels.

If this is right

  • Dual-encoder models that maintain separate language-specific representations achieve higher accuracy on structure and tense classification than single multilingual encoders.
  • The explicit structural and tense labels enable supervised training for controlled text generation and automated grammatical feedback.
  • The corpus supports cross-lingual representation learning by providing aligned bilingual supervision on the same grammatical dimensions.
  • Statistical distributions of structures and tenses in the data match patterns expected in natural language use.

Where Pith is reading between the lines

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

  • The annotation protocol could be reused with other language pairs to produce comparable gold-standard resources without starting from scratch.
  • High agreement on the chosen categories indicates that sentence structure and tense distinctions are stable enough for consistent labeling across languages.
  • Models trained with these labels may improve performance on downstream tasks that require explicit grammatical control, such as machine translation or summarization.

Load-bearing premise

The chosen encyclopedic and conversational sources supply representative samples of natural sentence structures and tenses in both languages without major domain bias or ambiguity in the annotation guidelines.

What would settle it

A replication in which new annotators following the published guidelines obtain Fleiss kappa scores below 0.7 on either dimension, or in which dual-encoder models no longer outperform multilingual baselines on a fresh test split drawn from the same sources.

Figures

Figures reproduced from arXiv: 2604.04708 by Abdullah Al Shafi, Abdul Muntakim, M. A. Moyeen, Shoumik Barman Polok, Swapnil Kundu Argha.

Figure 1
Figure 1. Figure 1: Flowchart of data collection and annotation of our proposed ‘BiST’ Corpus. Attribute Description and possible values Example Sentence The full Bangla or English sentence col￾lected from raw sources. Each sen￾tence occupies one row. ১৯৫২ সােলর ২১েশ েফবৰ্ুয়ািরেত এই আেন্দালন চূড়ান্ত রূপ ধারণ করেলও বস্তুত এর বীজ েরািপত হেয়িছল বহু আেগ; অনয্িদেক এর পৰ্িতিকৰ্য়া এবং ফলাফল িছল সুদূরপৰ্সারী। (Although this movement … view at source ↗
Figure 2
Figure 2. Figure 2: Word clouds representing the distribution of words across different ‘structure’ types in the corpus. (Currently, 2,112 active volunteers are working on Bangla Wikipedia.)’, ‘The boy with glasses was look￾ing at the moon.’, etc. (2) Complex: A sentence with one independent clause and at least one subordinate clause. Some examples include ‘েস পৰ্িতিদন সন্ধয্ায় খবর েদেখ যােত েস বতর্মান ঘটনার সম্পেকর্ আপেডট থা… view at source ↗
Figure 3
Figure 3. Figure 3: Word clouds representing the distribution of words across different ‘tense’ categories in the corpus. tence according to the pre-defined label sets S, and T. For each sentence si and annotator aj , the assigned labels are denoted sij ∈ S, and tij ∈ T. To ensure annotation reliability, dimension-wise agreement is computed using Fleiss’ Kappa. For each dimension d ∈ {S, T}, the item-wise agree￾ment Ai(d) is … view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of (a) structural types and (b) temporal categories in the developed corpus. 10 20 30 Sentence Length (words) 0.0 0.2 0.4 0.6 0.8 Density Simple Complex Compound Complex-compound (a) 10 20 30 Sentence Length (words) 0.00 0.05 0.10 0.15 0.20 Density Past Present Future (b) [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Kernel density estimation (KDE) for (a) structural classes and (b) temporal categories in the developed corpus. mon in everyday communication. In the temporal distribution (Fig. 4b), a more pronounced imbalance is evident. Present tense sentences account for 57.62% of the corpus, more than double the proportion of past (18.76%) and fu￾ture (23.62%) sentences. This skew mirrors real￾world linguistic usage, … view at source ↗
read the original abstract

High-quality bilingual resources remain a critical bottleneck for advancing multilingual NLP in low-resource settings, particularly for Bangla. To mitigate this gap, we introduce BiST, a rigorously curated Bangla-English corpus for sentence-level grammatical classification, annotated across two fundamental dimensions: syntactic structure (Simple, Complex, Compound, Complex-Compound) and tense (Present, Past, Future). The corpus is compiled from open-licensed encyclopedic sources and naturally composed conversational text, followed by systematic preprocessing and automated language identification, resulting in 30,534 sentences, including 17,465 English and 13,069 Bangla instances. Annotation quality is ensured through a multi-stage framework with three independent annotators and dimension-wise Fleiss Kappa ($\kappa$) agreement, yielding reliable and reproducible labels with $\kappa$ values of 0.82 and 0.88 for structural and temporal annotation, respectively. Statistical analyses demonstrate realistic structural and temporal distributions, while baseline evaluations show that dual-encoder architectures leveraging complementary language-specific representations consistently outperform strong multilingual encoders. Beyond benchmarking, BiST provides explicit linguistic supervision that supports grammatical modeling tasks, including controlled text generation, automated feedback generation, and cross-lingual representation learning. The corpus establishes a unified resource for bilingual grammatical modeling and facilitates linguistically grounded multilingual research.

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 BiST, a bilingual Bangla-English corpus of 30,534 sentences (17,465 English, 13,069 Bangla) compiled from open-licensed encyclopedic and conversational sources. Sentences are annotated for syntactic structure (Simple, Complex, Compound, Complex-Compound) and tense (Present, Past, Future) by three independent annotators using a multi-stage process. The work reports Fleiss' kappa values of 0.82 (structure) and 0.88 (tense), statistical analyses of label distributions, and baseline experiments where dual-encoder models outperform strong multilingual encoders. It positions BiST as a gold-standard resource supporting grammatical modeling, controlled text generation, and cross-lingual learning in low-resource multilingual NLP.

Significance. If the reported inter-annotator agreement holds and the distributions prove representative, BiST addresses a documented resource gap for Bangla by supplying explicit linguistic supervision for sentence-level grammatical classification. The concrete kappa values and dual-encoder baseline comparisons are strengths that support its use for benchmarking and downstream tasks. The multi-stage annotation framework and open-licensed sourcing further enhance potential reproducibility and utility in the field.

major comments (1)
  1. [Statistical analyses section] Statistical analyses section: The claim that the corpus exhibits 'realistic structural and temporal distributions' is not supported by any quantitative comparison of class proportions, sentence complexity metrics, or tense frequencies against reference corpora (e.g., full Wikipedia or established conversational benchmarks). Without such validation, the representativeness of the encyclopedic and conversational sources remains an untested assumption that directly affects the 'gold standard' characterization.
minor comments (2)
  1. [Methods section] Methods section: Provide explicit details on the automated language identification tool, preprocessing pipeline (including any filtering criteria), and exact annotation guidelines for distinguishing Complex-Compound sentences or handling tense in bilingual contexts to improve reproducibility.
  2. [Baseline evaluations] Baseline evaluations: Report the specific multilingual encoder models used, hyperparameter settings, data splits, and statistical significance tests for the performance differences to allow direct replication of the dual-encoder superiority claim.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the single major comment point by point below, with a commitment to revise where the concern is valid.

read point-by-point responses

  1. Referee: [Statistical analyses section] Statistical analyses section: The claim that the corpus exhibits 'realistic structural and temporal distributions' is not supported by any quantitative comparison of class proportions, sentence complexity metrics, or tense frequencies against reference corpora (e.g., full Wikipedia or established conversational benchmarks). Without such validation, the representativeness of the encyclopedic and conversational sources remains an untested assumption that directly affects the 'gold standard' characterization.

    Authors: We acknowledge that the manuscript presents the observed label distributions and frequencies within BiST but does not include direct quantitative comparisons against large external reference corpora such as full Wikipedia or established benchmarks. The description of 'realistic structural and temporal distributions' was intended to reflect the natural composition of the open-licensed encyclopedic and conversational sources, yet we agree this remains an assumption without explicit validation. In the revised version, we will remove the unsupported claim of realism and instead neutrally describe the empirical distributions observed in the corpus. We will also add a brief discussion noting the limitations of representativeness claims and, where feasible with available resources, include comparisons to smaller public subsets of conversational or encyclopedic data. The gold-standard characterization of BiST rests primarily on the multi-stage annotation protocol and the reported Fleiss' kappa values (0.82 for structure, 0.88 for tense), which are independent of distributional representativeness. revision: partial

Circularity Check

0 steps flagged

No significant circularity; empirical corpus construction with independent agreement metrics

full rationale

The paper reports the assembly of a bilingual corpus from open-licensed sources, multi-annotator labeling of sentence structure and tense, computation of Fleiss' kappa (0.82 structural, 0.88 temporal), descriptive statistics on class distributions, and baseline classifier results. No equations, fitted parameters, or predictions are present that reduce by construction to the inputs (e.g., no self-definitional scales, no 'prediction' of quantities used in fitting, no uniqueness theorems imported from self-citations). The central claims rest on observable annotation agreement and empirical distributions rather than any tautological loop. This is the expected non-finding for a resource-creation paper.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central contribution is empirical data curation rather than mathematical derivation; relies on standard inter-annotator agreement metrics and language identification tools without new free parameters or invented entities.

axioms (1)
  • standard math Fleiss Kappa is an appropriate measure of multi-annotator agreement for categorical labels
    Invoked when reporting κ values of 0.82 and 0.88

pith-pipeline@v0.9.0 · 5555 in / 1166 out tokens · 34053 ms · 2026-05-10T19:18:50.418351+00:00 · methodology

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Works this paper leans on

2 extracted references · 2 canonical work pages

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    Controllable text generation for large language models: A survey

    From matching to generation: A survey on generative information retrieval. ACM T rans- actions on Information Systems , 43(3):1–62. Xun Liang, Hanyu Wang, Yezhaohui Wang, Shichao Song, Jiawei Yang, Simin Niu, Jie Hu, Dan Liu, Shunyu Yao, Feiyu Xiong, et al. 2024. Controllable text generation for large language models: A survey. arXiv:2408.12599. Euan D Li...

    work page arXiv 2024