BackTranslation2.0 -- A Linguistically Motivated Metric to Assess Sign Language Production

• URL: http://arxiv.org/abs/2606.28673v1
• Date: Sat, 27 Jun 2026 01:24:56 GMT
• Title: BackTranslation2.0 -- A Linguistically Motivated Metric to Assess Sign Language Production
• Abstract summary: We introduce BackTranslation2.0, a linguistically grounded evaluation metric for text-to-sign translation.<n>We assess four scoring dimensions - grammatical correctness, phonological accuracy, motion fluency, and generation fidelity - aligned with human rater assessments.<n>Our method demonstrates strong correlation with human judgements across all dimensions, providing a more comprehensive, interpretable, and linguistically principled evaluation framework for sign language production systems.
• Score: 16.248107085488332
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Sign Languages (SLs) are the primary means of communication for millions of deaf individuals, yet existing evaluation metrics for generated SL remain simplistic and poorly aligned with human judgements. We introduce BackTranslation2.0, a linguistically grounded evaluation metric for text-to-sign translation that moves beyond naïve backtranslation. Our approach adopts an agentic framework in which a deterministic pipeline orchestrates a suite of specialised tools to assess four scoring dimensions - grammatical correctness, phonological accuracy, motion fluency, and generation fidelity - aligned with human rater assessments. Tool outputs are not treated independently: a set of large language model (LLM)-based cross-referential comparison modules evaluates consistency across tools and checks outputs against linguistic expectations, enabling structured reasoning over grammatical, phonological, and motion-level evidence. Final dimension scores are computed through deterministic weighted formulas over validated tool outputs. To validate BackTranslation2.0, we introduce and evaluate on a British Sign Language (BSL) dataset rated in a human rater study across the same quality dimensions, following a protocol developed in collaboration between linguists and deaf experts, benchmarking against six baseline metrics. Our method demonstrates strong correlation with human judgements across all dimensions, providing a more comprehensive, interpretable, and linguistically principled evaluation framework for sign language production systems.

Related papers

• EviSI: An Evidence-Based Evaluation Agent for Simultaneous Interpreting [15.392286631815763]
  EviSI is a large language model evaluation agent combining Multidimensional Quality Metrics (MQM) with criteria developed with professional interpreters.<n>On human-rated English to Chinese and Chinese to English data, EviSI recovers the aggregate English to Chinese human system ranking.<n>A multilingual extension to five directions without human ratings retains the dimensions and scoring rule, showing positive system ranking correlations with COMET throughout.
  arXiv  Detail & Related papers  (2026-09-08T03:00:11Z)
• How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework [0.47954782871066737]
  We propose a context-aware evaluation framework in which human-likeness is assessed.<n>We implement this framework using the Maximum Mean Discrepancy (MMD) and the 67 lexico-grammatical features introduced by Biber.
  arXiv  Detail & Related papers  (2026-05-22T14:04:25Z)
• XQ-MEval: A Dataset with Cross-lingual Parallel Quality for Benchmarking Translation Metrics [64.77152900881724]
  We propose XQ-MEval, a semi-automatically built dataset covering nine translation directions.<n>We inject MQM-defined errors into gold translations automatically, filter them by native speakers for reliability, and merge errors to generate pseudo translations with controllable quality.<n>Using XQ-MEval, our experiments on nine representative metrics reveal the inconsistency between averaging and human judgment.
  arXiv  Detail & Related papers  (2026-04-16T12:27:10Z)
• Cross-Lingual LLM-Judge Transfer via Evaluation Decomposition [13.066970846043576]
  We introduce a decomposition-based evaluation framework built around a Universal Criteria Set (UCS)<n>UCS consists of a shared, language-agnostic set of evaluation dimensions, producing an interpretable intermediate representation.<n>Experiments on multiple faithfulness tasks across languages and model backbones demonstrate consistent improvements without requiring target-language annotations.
  arXiv  Detail & Related papers  (2026-03-19T07:11:31Z)
• On the Fallacy of Global Token Perplexity in Spoken Language Model Evaluation [88.77441715819366]
  Generative spoken language models pretrained on large-scale raw audio can continue a speech prompt with appropriate content.<n>We propose a variety of likelihood- and generative-based evaluation methods that serve in place of naive global token perplexity.
  arXiv  Detail & Related papers  (2026-01-09T22:01:56Z)
• Towards Corpus-Grounded Agentic LLMs for Multilingual Grammatical Analysis [0.5545791216381869]
  We explore how agentic large language models (LLMs) can streamline the systematic analysis of annotated corpora.<n>We introduce an agentic framework for corpus-grounded grammatical analysis that integrates concepts such as natural-language task interpretation.<n>We test the system on multilingual grammatical tasks inspired by the World Atlas of Language Structures (WALS)
  arXiv  Detail & Related papers  (2025-11-28T21:27:58Z)
• Does Language Model Understand Language? [1.0450509067356148]
  Despite advances in natural language generation and understanding, LM still struggle with fine grained linguistic phenomena.<n>In this study, we conduct a evaluation of SOTA language models across challenging contexts in both English and Bengali.<n>Our findings highlight Compound-Beta as the most balanced model, consistently achieving high correlations and low MAEs across diverse language conditions.
  arXiv  Detail & Related papers  (2025-09-15T21:09:09Z)
• Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling [70.23876429382969]
  We propose a benchmark that can evaluate intra-sentence discourse properties across a diverse set of NLP tasks. Disco-Bench consists of 9 document-level testsets in the literature domain, which contain rich discourse phenomena. For linguistic analysis, we also design a diagnostic test suite that can examine whether the target models learn discourse knowledge.
  arXiv  Detail & Related papers  (2023-07-16T15:18:25Z)
• A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space [61.18554842370824]
  In cross-lingual language models, representations for many different languages live in the same space. We compute a task-based measure of cross-lingual alignment in the form of bitext retrieval performance. We examine a range of linguistic, quasi-linguistic, and training-related features as potential predictors of these alignment metrics.
  arXiv  Detail & Related papers  (2021-09-13T21:05:37Z)
• AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
  We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
  arXiv  Detail & Related papers  (2021-04-17T20:23:45Z)
• TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing [73.16475763422446]
  We propose a multilingual robustness evaluation platform for NLP tasks (TextFlint) It incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analysis. TextFlint generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model's robustness.
  arXiv  Detail & Related papers  (2021-03-21T17:20:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.