Mara Nunziatini


All You Need is Source! A Study on Source-based Quality Estimation for Neural Machine Translation
Jon Cambra | Mara Nunziatini
Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)

Segment-level Quality Estimation (QE) is an increasingly sought-after task in the Machine Translation (MT) industry. In recent years, it has experienced an impressive evolution not only thanks to the implementation of supervised models using source and hypothesis information, but also through the usage of MT probabilities. This work presents a different approach to QE where only the source segment and the Neural MT (NMT) training data are needed, making possible an approximation to translation quality before inference. Our work is based on the idea that NMT quality at a segment level depends on the similarity degree between the source segment to be translated and the engine’s training data. The features proposed measuring this aspect of data achieve competitive correlations with MT metrics and human judgment and prove to be advantageous for post-editing (PE) prioritization task with domain adapted engines.


A Synthesis of Human and Machine: Correlating “New” Automatic Evaluation Metrics with Human Assessments
Mara Nunziatini | Andrea Alfieri
Proceedings of Machine Translation Summit XVIII: Users and Providers Track

The session will provide an overview of some of the new Machine Translation metrics available on the market, analyze if and how these new metrics correlate at a segment level to the results of Adequacy and Fluency Human Assessments, and how they compare against TER scores and Levenshtein Distance – two of our currently preferred metrics – as well as against each of the other. The information in this session will help to get a better understanding of their strengths and weaknesses and make informed decisions when it comes to forecasting MT production.


Machine Translation Post-Editing Levels: Breaking Away from the Tradition and Delivering a Tailored Service
Mara Nunziatini | Lena Marg
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

While definitions of full and light post-editing have been around for a while, and error typologies like DQF and MQM gained in prominence since the beginning of last decade, for a long time customers tended to refuse to be flexible as for their final quality requirements, irrespective of the text type, purpose, target audience etc. We are now finally seeing some change in this space, with a renewed interest in different machine translation (MT) and post-editing (PE) service levels. While existing definitions of light and full post-editing are useful as general guidelines, they typically remain too abstract and inflexible both for translation buyers and linguists. Besides, they are inconsistent and overlap across the literature and different Language Service Providers (LSPs). In this paper, we comment on existing industry standards and share our experience on several challenges, as well as ways to steer customer conversations and provide clear instructions to post-editors.


Machine Translation in the Financial Services Industry: A Case Study
Mara Nunziatini
Proceedings of Machine Translation Summit XVII: Translator, Project and User Tracks