machine-translation

This paper investigates whether Arabic character form-function relationships are arbitrary from an NLP perspective, showing that random character remappings to reduced rasm sets can achieve competitive performance across various NLP tasks.

TabletCraft is an open-source system enabling bidirectional Akkadian-English neural machine translation with cuneiform rendering, allowing users to both read ancient tablets and compose new messages in cuneiform. Accepted to the C3NLP workshop at ACL 2026, it reports first published quantitative results for English-to-Akkadian translation.

This paper studies quantization trade-offs for deploying translation models like EuroLLM and Hy-MT2 on a single GPU, showing that combining document chunking with W4A8/W8A8 quantization improves latency-throughput trade-offs while introducing a document-level evaluation to assess long-context dynamics.

This paper studies cross-lingual transfer for machine translation among five Turkic languages using pairwise transfer matrices with mT5, finding that transfer is strongest between closely related pairs and that Latinization helps in script-mismatched settings.

This paper introduces Translation with Thought (TwT), a resource-rational framework for multi-domain machine translation that adaptively modulates reasoning effort based on input difficulty, trained via supervised fine-tuning on difficulty-aware reasoning traces and reinforcement learning. TwT-7B and TwT-14B outperform larger SOTA reasoning models while reducing token usage by 32–60%.

Introduces Contrastive Error Span Annotation (cESA), a protocol for human evaluation of multiple translations simultaneously, reducing annotation time and noise compared to standard methods.

This paper evaluates how prompt scope (single-target vs. family-scope) and demonstration similarity (random, lexical, embedding) affect the translation quality of local LLMs for English-to-Romance and English-to-Germanic languages, finding that dedicated MT systems still outperform and that embedding retrieval offers modest gains.

This paper introduces GAND, a benchmarking resource of gender-ambiguous natural English sentences for analyzing gender bias in machine translation, and presents an interpretability analysis using contrastive translations to reveal source words influencing gender assignment.

This paper presents a systematic study of cross-lingual transfer for biomedical machine translation into low-resource Arabic-script languages, using Arabic and Persian as pivots. The authors evaluate LoRA adapter merging as a zero-data transfer strategy, showing it works surprisingly well for closely related languages like Dari.

This paper proposes a cross-lingual data augmentation strategy that uses machine translation to transfer expert-annotated difficulty labels from high-resource languages to low-resource languages. Experiments with BERT-based regression models show that augmenting scarce native data with translated corpora significantly improves the accuracy of text difficulty assessment.

LatentMT applies latent-reasoning looped language models to machine translation, achieving performance comparable to models three to five times larger while requiring lower compute, and sets state-of-the-art on mid- and low-resource languages.

This paper investigates how multilingual tokenizers handle cross-lingual homographs (identical surface forms with different meanings across languages) and proposes a lightweight language-cue intervention that introduces language-specific characters to reduce token sharing. Experiments show modest improvements in machine translation, particularly with BPE tokenization.

Proposes Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm for faithful generation that reframes post-training as token-level correctness prediction, achieving strong out-of-distribution generalization across summarization and machine translation tasks.

This paper investigates the feasibility of using translation-based fine-tuning as a resource-efficient alternative to native-language BERT models for low-resource languages, finding it comparable or superior in 53.3% of cases across six NLP tasks.

This paper proposes CoPiT, a cognitively motivated pivot-based translation pipeline for digraphic Mongolian that routes translation through the better-resourced Cyrillic script to improve translation from the low-resource Traditional script, achieving significant BLEU and COMET gains and releasing a new multi-script parallel dataset.

This study investigates how prompt language and translation-theory-driven prompt design affect the quality of Spanish-Chinese journalistic translations produced by GPT-5.2, finding that theory-driven prompts improve style under expert evaluation despite automated metrics favoring baseline prompts.

An 18-year-old Tunisian student introduces an open-source machine translation pipeline and parallel corpus for Tunisian Darija in Arabizi script, built from scratch with a small 15.6M-parameter Transformer and an honest baseline BLEU of 3.89, and calls for contributors to ethically expand the corpus.

This paper examines the deployment of an LLM-based machine translation system for text-to-911 emergency services, highlighting common misconceptions and providing recommendations for stakeholders to ensure safe and effective use of AI in critical contexts.

This paper introduces MultiSynt/MT, a trillion-token multilingual parallel corpus created by translating English pre-training data into 36 languages. Experiments show that LLMs trained on this translated data achieve performance comparable to native data with fewer tokens, though some evaluation blind spots and cultural gaps remain.

MetaHOPE is a metaphor-oriented evaluation framework for analyzing translation errors in machine translation and large language models. The paper proposes an error severity-aware annotation framework and evaluates models like GoogleMT, GPT5.4, and Hunyuan-7b on English-Chinese metaphor translation.