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Translation as a Decision Space: A Multi-Agent Perspective on Low-Resource Dialect Generation

Hasan Alkhder, Mohammad Abboush, Igor Tchappi, Ahmet Zengin, Amro Najjar

arXiv:2609.04048v1cs.CLcs.AI

TL;DR

The paper addresses how to analyze alternative translation decisions that remain hidden when NMT systems expose only one output, especially for low-resource dialects. It models controlled translation pathways as agents over a shared multilingual backbone and measures their behavioral divergence. Lightweight stabilization nearly doubles dialect marker frequency while reducing structural variance, whereas pivot mediation produces normalization and compression effects.

  • Problem

    NMT systems typically expose one output despite multilingual decoding containing alternative trajectories, limiting analysis of valid dialectal differences in authenticity, register, and structure.

  • Method

    The study models zero-shot, lightweight dialect-stabilized, and English-pivoted Turkish–Syrian translation pathways as constrained agents sharing one multilingual backbone.

  • Results

    Lightweight stabilization nearly doubles dialect marker frequency from 0.2266 to 0.4988 while significantly reducing structural variance; pivot mediation introduces normalization and compression effects.

  • Takeaways & Limitations

    Inter-agent divergence provides an interpretable signal of controlled displacement in lexical weighting and structural realization within multilingual translation.

  • Takeaways & Limitations

    Empirical validation is limited to Turkish–Syrian Arabic, and the divergence-focused metrics do not balance authenticity with translation quality.

Abstract

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Neural machine translation (NMT) systems typically produce a single output per input, obscuring the alternative decision trajectories implicitly available within multilingual decoding. This opacity becomes particularly problematic in low-resource dialect settings, where multiple linguistically valid realizations may differ in lexical authenticity, register, and structural stability. We propose reframing translation as a structured decision space explored by autonomous translation agents. Instead of analyzing a single output, we model distinct translation pathways as agents operating over a shared multilingual backbone. Inter-agent divergence is treated not as error but as an interpretable behavioral signal. We conduct an empirical study on Turkish--Syrian Arabic translation using three agents: (1) zero-shot direct translation, (2) dialect-stabilized translation via lightweight fine-tuning, and (3) pivot translation through English. Evaluation is performed on 5,000 dialogue sentences, while stabilization is trained on 5,000 additional Turkish--Syrian sentence pairs drawn from television dialogue and MADAR-Turk resources. Rather than optimizing for conventional performance metrics, we quantify structured behavioral displacement using dialect marker frequency, lexical proximity to standardized Arabic, and structural variance. Lightweight stabilization nearly doubles dialect marker usage, increasing it from 0.2266 to 0.4988, while significantly reducing structural instability. Pivot mediation introduces normalization pressure and measurable compression effects, whereas zero-shot translation exhibits the highest decision variance. We argue that translation divergence across agents reveals latent decision flexibility within multilingual models and we provide a principled interpretability framework for low-resource dialect generation.

1 Introduction

The paper reframes low-resource dialect translation as a structured decision space whose alternative pathways can be analyzed as constrained agents. It studies Turkish–Syrian Arabic to measure how zero-shot, stabilized, and pivot pathways reshape dialectal and structural behavior.

  • NMT systems expose one translation output even though multilingual decoding contains multiple plausible trajectories.
  • Dialect translation requires analyzing lexical, social, stylistic, register, authenticity, and structural differences that conventional adequacy metrics do not fully capture.
  • The framework models zero-shot, dialect-reinforced, and pivot-mediated pathways as constrained agents operating over the same multilingual backbone.
  • Inter-agent divergence is treated as structured behavioral signal rather than error, revealing how pathways reshape dialect activation, lexical normalization, and structural realization.
  • The study uses translation as a controlled generative environment for analyzing pathway-level divergence rather than optimizing machine translation accuracy.

2 Related Work

Prior work improves low-resource translation quality through transfer learning, pivoting, dialect resources, explainability, and multi-agent collaboration. However, these approaches generally expose limited insight into alternative translation trajectories and the decisions underlying them.

  • Transfer-learning studies target data-scarce translation by transferring knowledge from higher-resource or related language pairs.
  • Syrian Arabic resources and dialect benchmarks address dialect translation and identification, but most systems still treat translation as a single-stage mapping.
  • Explainability methods commonly provide post-hoc interpretations of single outputs rather than exposing broader translation-generation processes.
  • Existing multi-agent translation methods typically use separate models for collaborative refinement rather than exploring structured alternatives within one translation system.
  • Pivot-based methods exploit high-resource intermediate languages, but can propagate errors across stages and provide limited insight into pivot-mediated decisions.
  • The identified research gap is limited interpretability of alternative translation hypotheses and internal decision processes in low-resource dialect translation.

3 Data Resources and Experimental Setup

The experimental setup compares translation pathways on a fixed, independently constructed 5K dialogue evaluation corpus with aligned Turkish, Syrian Arabic, English, and MSA representations. Stabilization uses a separate 5K Turkish–Syrian corpus and intentionally lightweight tuning.

  • The evaluation corpus contains 5K sentence-level dialogue segments used for controlled behavioral comparison across pathways.
  • Each entry aligns Turkish source text, Syrian Arabic reference dialogue, English pivot text, and MSA text for dialectal and normalization analyses.
  • The evaluation data combines 3K naturally occurring television-dialogue segments with 2K structurally controlled MADAR-Turk conversational pairs.
  • Evaluation and stabilization sentences are independent, preventing sentence-level overlap and supporting generalization rather than memorization.
  • Stabilization uses a separate 5K Turkish–Syrian corpus combining television dialogue and MADAR-Turk resources.
  • Fine-tuning runs for one epoch at 1×10^-5 without vocabulary or architectural changes, constraining shifts primarily to lexical reweighting.

4 Multi-Agent Translation Framework

The framework treats one multilingual model as generating alternative realizations under controlled pathway constraints. Three agents—zero-shot, dialect-stabilized, and English-pivoted—sample neighboring regions of a shared translation decision space whose divergence is analyzed relationally.

  • Multilingual translation is modeled as exploration of a conditional output distribution rather than a single deterministic mapping.
  • The decision space consists of observable outputs produced when the same model is executed under different controlled pathway constraints.
  • Each agent applies a pathway constraint to the fixed multilingual model without changing its architecture.
  • The three pathways are direct zero-shot translation, lightweight dialect stabilization, and Turkish→English→Syrian pivot mediation.
  • All agents share decoding parameters and evaluation data, so pathway constraints are the intended source of behavioral variation.
  • The observed agent outputs form a subset of the full output space and are interpreted as neighboring semantic realizations rather than correct-versus-incorrect alternatives.
  • Relational output analysis uses divergence to examine dialectal authenticity, normalization pressure, and structural stability without directly probing hidden representations.

5 Behavioral Metrics for Structured Divergence

The paper complements reference-based adequacy evaluation with behavior-oriented metrics that quantify how constrained translation pathways differ in dialect activation, normalization, and structural realization. These measures support relational analysis of outputs rather than single-output correctness alone.

  • Behavior-oriented evaluation measures how translation outputs shift under different decoding conditions instead of evaluating one output against a reference.The framework treats structured divergence within the translation decision space as the object of analysis.
  • Dialect marker frequency reflects lexical-level dialect activation, lexical overlap captures normalization tendencies, and structural length variation reflects generation stability.Together, these metrics represent complementary behavioral dimensions.
  • 5.1 Dialect Marker Frequency: Dialect activation uses a curated set of Syrian dialect markers validated by native speakers and reports corpus-level marker usage for each agent.Higher activation values indicate stronger dialect reinforcement in generated translations.
  • 5.2 Lexical Proximity to Standardized Arabic: Lexical proximity compares generated outputs with standardized Arabic to estimate normalization pressure, with lower overlap indicating stronger dialectal divergence.The comparison uses a standardized reference and token overlap.
  • 5.3 Structural Length Ratio: Structural realization is measured through relative sentence length against the standardized reference, while higher variance indicates instability across generated translations.The metric captures variation in structural realization across an agent’s outputs.
  • 5.4 Inter-Agent Divergence: Inter-agent displacement combines divergence in dialect activation, lexical normalization, and structural realization with equal weighting.Equal weighting avoids privileging one behavioral dimension because the components capture complementary aspects of translation behavior.

6 Quantitative Results

Across a shared multilingual backbone, the three translation pathways show stable, distinct behavioral profiles. Stabilization increases dialect activation and improves structural stability, while pivot mediation compresses output and introduces normalization effects.

  • All agents share the same multilingual backbone, so aggregate differences reflect pathway-induced decision reweighting rather than architectural variation.Paired bootstrap comparisons found dialect-marker and length-ratio differences stable across resampled evaluation subsets.
  • Dialect stabilization increases average dialect marker frequency from 0.2266 to 0.4988 while reducing structural variance from σL = 2.8761 to 1.1834.Lexical overlap with standardized Arabic also decreases from 0.2133 to 0.1117.
  • Pivot translation produces moderate dialect activation at 0.2578 and a mean length ratio of 0.9354, indicating compression relative to standardized references.Its variance is lower than zero-shot but slightly higher than stabilized translation.
  • The three agents occupy distinct behavioral regions, with zero-shot showing high variance, stabilization reinforcing dialect forms, and pivot translation compressing output length.These patterns are consistent with pathway-specific behavioral differences.
  • Increased dialectal activation does not appear to systematically degrade semantic adequacy, although minor lexical deviations occur while core meaning is generally preserved.

7 Discussion: Decision-Space Reconfiguration Across Agents

The agents produce systematic behavioral displacement within a shared decision space rather than stochastic variation. Their distinct profiles expose how decoding constraints redistribute lexical and structural weighting across dialect activation, stability, and normalization.

  • The three agents’ systematic behavioral displacement reflects pathway-induced reweighting because they operate over the same backbone architecture.
  • Zero-shot translation has the highest structural variance at σL = 2.8761 and the lowest dialect marker activation at 0.2266.The paper interprets this pattern as under-specified dialect representation and unstable oscillation between standardized and colloquial realizations.
  • Dialect stabilization nearly doubles marker frequency to 0.4988 while reducing structural variance to σL = 1.1834 after lightweight tuning.The reported regime uses a single epoch and low learning rate, consistent with lexical probability redistribution rather than structural retraining.
  • Pivot mediation yields a mean length ratio of 0.9354 and moderate dialect activation of 0.2578, imposing normalization pressure through an intermediate English representation.The pathway frequently compresses emotionally loaded or colloquial constructions.
  • Inter-agent divergence is treated as an interpretable signal of how multilingual systems negotiate authenticity, normalization, and mediation under different constraints.Figure 2 summarizes stabilization as dialect reinforcement with reduced structural expansion and pivot mediation as compression with moderate normalization.

8 Sentence-Level Divergence Analysis

Sentence-level inspection examines examples with the highest inter-agent divergence to show how pathway constraints reshape lexical choice, syntax, and interpretation. The qualitative patterns align with aggregate evidence of structured, rather than noisy, variation.

  • The selected examples come from sentences with the highest inter-agent divergence scores, where pathway-induced behavioral displacement is most pronounced.
  • Side-by-side outputs enable direct inspection of how pathway constraints reshape lexical choice, syntactic realization, and semantic interpretation within one multilingual model.
  • Zero-shot outputs show lexical ambiguity and structural under-specification, whereas stabilization increases colloquial activation and syntactic completeness.Pivot mediation often compresses emotionally marked constructions and can introduce semantic reinterpretation through English normalization.
  • These qualitative observations agree with the quantitative patterns, indicating structured behavioral variation rather than stochastic decoding noise.

9 Implications for Explainable Multi-Agent Systems

The framework makes explainability emerge from controlled variation among translation pathways rather than from inspecting internal model states. This relational approach exposes how lexical and structural weighting shifts within a shared architecture.

  • The framework generalizes to generative decision systems in which behavior can be interpreted through agent-level pathway variation.
  • Interpretability is constructed through relational comparison among direct, stabilized, and pivot-mediated pathways instead of extracted from internal activations or attention maps.
  • Controlled pathway variation is especially valuable in low-resource settings, where ambiguity and variability are intrinsic rather than exceptional.
  • Selective reinforcement or mediation exposes shifts in lexical and structural weighting within the same architecture.

10 Limitations

The study’s limitations constrain its empirical scope, adaptation setting, evaluation emphasis, and lexical measurements. These boundaries leave generalizability, alternative adaptation dynamics, adequacy trade-offs, and orthographic robustness for future work.

  • The empirical validation is limited to Turkish–Syrian Arabic, so generalizability across other language pairs and dialect continua remains unconfirmed.The framework is described as conceptually transferable, but additional empirical validation is required.
  • Dialect stabilization used intentionally lightweight adaptation, leaving the structural effects of more extensive adaptation unexplored.The setup used one epoch and a low learning rate to isolate lexical reweighting effects.
  • The behavioral metrics emphasize divergence rather than adequacy, motivating future integration of performance-oriented measures.Such integration is proposed to balance authenticity with translation quality.
  • Lexical overlap calculations may be affected by orthographic variation in Arabic dialect writing despite controlled preprocessing.Dialect spelling variability remains an inherent challenge in low-resource settings.

11 Conclusion

The paper models translation pathways as constrained agents within a shared multilingual representation space and analyzes their behavioral displacement. Across 5,000 evaluation sentences, stabilization amplified dialect markers and reduced structural instability, while pivot mediation produced normalization and compression effects; the framework treats these divergences as interpretability signals.

  • The framework represents zero-shot, stabilized, and pivot-mediated translation pathways as constrained agents in a shared multilingual representation space.
  • Across 5,000 evaluation sentences, lightweight stabilization amplified dialect marker activation while reducing structural instability.
  • Pivot mediation introduced measurable normalization and compression effects, contrasting with the stabilized pathway’s behavioral shifts.
  • Inter-agent divergence serves as an interpretability signal for how multilingual models balance dialectal authenticity, structural realization, and semantic mediation.
  • Controlled pathway variation may extend this framework to additional dialect continua and generative tasks.
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