The year 2024 marks the 10th anniversary of the Multidimensional Quality Metrics (MQM) framework for analytic translation quality evaluation. The MQM error typology has been widely used by practitioners in the translation and localization industry and has served as the basis for many derivative projects. The annual Conference on Machine Translation (WMT) shared tasks on both human and automatic translation quality evaluations used the MQM error typology. The metric stands on two pillars: error typology and the scoring model. The scoring model calculates the quality score from annotation data, detailing how to convert error type and severity counts into numeric scores to determine if the content meets specifications. Previously, only the raw scoring model had been published. This April, the MQM Council published the Linear Calibrated Scoring Model, officially presented herein, along with the Non-Linear Scoring Model, which had not been published

In the 2023 edition of the ASTM International translation standard (F2575) the labels BRT and UMT have been standardized. The Label BRT stands for ‘Bilingually Reviewed Translation, by a qualified language professional’. The Label UMT is for everything else, from raw machine translation to MT where only the target text is checked, to human translation that does not involve a qualified professional. Thus, UMT could be expanded as ‘Unreviewed or Missing-qualifications Translation’. This presentation will argue that the use of the labels BRT and UMT is a triple win: The ‘consumers’ (end users) of a translation win because they have useful information for risk analysis (harm from errors). MT developers win because they have useful metadata when selecting training material. And professional translators win by increasing their visibility to the public. The presentation will give a history of these two labels and enlist the help of the entire AMTA community in promoting their use.

We present a design for a multi-modal database system for lexical information that can be accessed in either lexicographical or terminological views. The use of a single merged data model makes it easy to transfer common information between termbases and dictionaries, thus facilitating information sharing and re-use. Our combined model is based on the LMF and TMF metamodels for lexicographical and terminological databases and is compatible with both, thus allowing for the import of information from existing dictionaries and termbases, which may be transferred to the complementary view and re-exported. We also present a new Linguistic Configuration Model, analogous to a TBX XCS file, which can be used to specify multiple language-specific schemata for validating and understanding lexical information in a single database. Linguistic configurations are mutable and can be refined and evolved over time as understanding of documentary needs improves. The system is designed with a client-server architecture using the HTTP protocol, allowing for the independent implementation of multiple clients for specific use cases and easy deployment over the web.

Post-editing of machine translation has become more common in recent years. This has created the need for a formal method of assessing the performance of post-editors in terms of whether they are able to produce post-edited target texts that follow project specifications. This paper proposes the use of formalized structured translation specifications (FSTS) as a basis for post-editor assessment. To determine if potential evaluators are able to reliably assess the quality of post-edited translations, an experiment used texts representing the work of five fictional post-editors. Two software applications were developed to facilitate the assessment: the Ruqual Specifications Writer, which aids in establishing post-editing project specifications; and Ruqual Rubric Viewer, which provides a graphical user interface for constructing a rubric in a machine-readable format. Seventeen non-experts rated the translation quality of each simulated post-edited text. Intraclass correlation analysis showed evidence that the evaluators were highly reliable in evaluating the performance of the post-editors. Thus, we assert that using FSTS specifications applied through the Ruqual software tools provides a useful basis for evaluating the quality of post-edited texts.

The standard design for a computer-assisted translation system consists of data entry of source text, machine translation, and post editing (i.e. revision) of raw machine translation. This paper discusses this standard design and presents an alternative three-level design consisting of word processing integrated with terminology aids, simple source text processing, and a link to an off-line machine translation system. Advantages of the new design are discussed.