Translation-Service

Analysis of degeneracies in inverse propensity scoring for offline machine translation learning from deterministic logs, with proposed solutions and formal insights.

Analysis of the first application of Neural Machine Translation to Arabic, comparing performance against phrase-based systems and evaluating preprocessing effects.

Analysis of using LLMs for few-shot translation of English semantic parsing datasets to train multilingual parsers, outperforming translate-train baselines across 50 languages.

An in-depth exploration of Neural Machine Translation, covering its history, core neural network concepts, language modeling, encoder-decoder architectures, refinements, and future challenges.

Analysis of a novel post-training domain adaptation method for NMT, exploring incremental specialization, experimental results, and future applications.

An analysis of challenges and opportunities in legal translator training, exploring updated models, technology integration, and the evolving role of translators as intercultural mediators.

An analysis of how professional translation ethics codes apply to non-professional contexts like crowdsourcing, fan translation, and FOSS localization, highlighting differences and innovations.

Analysis of TM-augmented NMT from a probabilistic retrieval view and variance-bias decomposition, proposing a method to address contradictory performance in high/low-resource scenarios.

Analysis of how globalization and technology redefine translation demands, positioning translators as cultural mediators and strategic business assets.

Analysis of a variational encoder-decoder model for NMT that introduces continuous latent variables to explicitly model underlying semantics, improving translation quality.