Advancing Translation Skills: How to Efficiently Post-Edit with BWX Generative AI-Translation Technology

A Comprehensive Review of the ELIA Together Workshop

On February 21st, 2024, linguists from diverse backgrounds congregated in Riga for the MTPE Workshop, sponsored by Bureau Works . This immersive event aimed to elevate participants' proficiency in AI Translation post-editing, harnessing the potential of BWX Generative AI-Translation Technology.

Pre-Workshop Exercise: Preparing Minds for Mastery

The workshop kicked off with a meticulously crafted pre-workshop exercise, providing attendees with invaluable hands-on experience in annotating errors in AI translations. Through the BWX platform, participants engaged with Translation Memories, Machine Translation, Glossaries, and Generative AI, laying the foundation for deeper exploration during the workshop. This exercise served not only as a precursor to the main event but also fostered active learning, enabling participants to familiarize themselves with the collaborative systems effectively.

Diverse Linguistic Exploration: A Multifaceted Journey

Diversity was at the heart of the workshop, as participants embarked on exercises spanning 10 different languages. This diverse landscape invaluable insights into error categorization specific to each language. As an example, while English and Spanish participants may encounter similar challenges with terminology, nuances in grammar and syntax may vary significantly between translations into German and French.

Hands-On Learning and Expertise: Sharpening Skills Through Practice

Participants tackled a pre-workshop exercise totaling an impressive 59 tasks, showcasing their dedication and expertise in the field. By the day of the workshop, 12 exercises were successfully completed, underscoring the meticulous attention to detail required in post-editing. These hands-on exercises allowed participants to apply theoretical knowledge in practical scenarios, honing their decision-making skills and problem-solving abilities.

Analysis of Results: Unveiling Insights Through Data

The workshop's data-driven approach provided profound insights into AI/MT errors and post-editing effort across various language pairs. Let's break down the findings:

1. Error Distribution by Language Pair: The workshop data reveals variations in error distribution across different language pairs. For instance, while English to Dutch (EN>NL) and English to Lithuanian (EN>LT) translations exhibited higher frequencies of incorrect translations and terminology errors, English to Italian (EN>IT) translations demonstrated lower error rates in these categories. This suggests that certain language pairs may present unique challenges in AI Translation post-editing, influenced by linguistic, cultural, or contextual factors, the training of the MT and LLMs, but most significantly by the skills and expertise of translators.

2. Correlation between Error Types: By examining the correlation between error types, we can uncover underlying patterns in AI/MT output. For example, a strong correlation between terminology errors and incorrect translations may indicate challenges in accurately capturing domain-specific vocabulary. Similarly, a correlation between grammar errors and fluency disruptions may suggest issues related to syntactic coherence. Understanding these correlations allows linguists to address root causes of errors and develop targeted strategies for improvement.

3. Impact of Error Frequency on Post-Editing Effort: The data highlights a clear relationship between error frequency and post-editing effort. Segments with higher error frequencies typically require more extensive revisions, leading to increased post-editing effort. This underscores the importance of prioritizing resources and allocating sufficient time for segments with higher error rates. Additionally, identifying segments with recurring errors enables linguists to implement preventive measures and streamline post-editing workflows.

4. Effectiveness of Post-Editing Strategies: Evaluating the effectiveness of post-editing strategies is essential for optimizing workflow efficiency and improving translation quality. The data allows us to assess the impact of the BWX AI-powered features, AI Translation, AI Automations and synergy of context on the different post-editing approaches and on error reduction rates. For example, comparing the effectiveness of manual post-editing versus automated correction tools provides insights into the most efficient strategies. Additionally, analyzing post-editing effort relative to error reduction rates enables stakeholders to identify areas for process optimization and skill development.

5. Long-Term Trends in LLMs/MT Performance: Tracking long-term trends in LLMs/MT performance is crucial for assessing the evolution of translation technologies and informing strategic decision-making. By comparing error rates, post-editing effort, and translation quality metrics across multiple workshops or projects, stakeholders can identify areas of improvement and measure the effectiveness of ongoing enhancements. This longitudinal analysis provides valuable insights into the trajectory of AI Translation technology and facilitates data-driven planning for future initiatives.

The Impact of BWX on the Minimization of Effort

With BWX Generative AI-Translation Technology, post-editors can efficiently address common errors encountered during the post-editing process. The platform's features, including the BWX Quality Professor, facilitate the resolution of stylistic preferential changes, under-edited or over-edited translations by providing real-time feedback and guidance. Additionally, the Add Terms on the Fly functionality assists in resolving inconsistencies in terminology and style, ensuring a cohesive and professional translation output. Furthermore, BWX Check Smells identifies and rectifies issues such as unlocalized text, errors on the structural level, and misuse of "false cognates," enhancing the overall accuracy and readability of the translation. Finally, BWX Proofreading and Fix Tags streamline the correction of tags, untranslated, added, or omitted words, typos, and grammar errors, enabling post-editors to deliver high-quality translations efficiently and effectively.

Conclusions and Implications: Navigating the Road Ahead

The analysis of LLMs/MT errors underscores the critical role of human expertise in refining machine-generated translations. Variations in performance across languages highlight the necessity for tailored post-editing strategies. For instance, while the English to Dutch (EN>NL) language pair may require extensive changes, the English to Italian (EN>IT) pair may demand fewer modifications. Additionally, the workshop showcased how BWX Generative AI-Translation Technology can empower Language Service Providers (LSPs) and translators to evaluate engine performance comprehensively.

Towards Fair Compensation Models: Bridging the Gap

By prioritizing thinking time over editing time, the BWX platform advocates for a fairer compensation model that recognizes the intellectual labor of linguists. This paradigm shift fosters equity and transparency within the translation ecosystem. LSPs and translators can leverage the insights gained from this workshop to advocate for fair compensation based on the cognitive effort expended, promoting a more equitable distribution of resources within the industry.

Leveraging Data for Continuous Improvement: Unlocking Potential

Beyond the immediate implications, the workshop's data analysis opens doors to continuous improvement and innovation. By leveraging error reports and performance metrics, participants and industry stakeholders can identify trends, refine strategies, and optimize workflows. This data-driven approach not only enhances the quality of the output but also empowers linguists to provide more accurate assessments and recommendations.

Fostering Collaboration and Knowledge Sharing: Building Community

The workshop served as a platform for collaboration and knowledge sharing among linguists, LSPs, and technology providers. By facilitating open dialogue and sharing best practices, participants forged valuable connections and contributed to a collective pool of expertise. This collaborative spirit fosters a culture of innovation and continuous learning, driving positive change within the translation industry.

Looking Ahead: Charting a Course for Progress

In conclusion, the BWX Workshop was a resounding success, empowering linguists to navigate the evolving landscape of AI Translation with confidence and expertise. As participants continue to refine their skills and embrace emerging technologies, they are driving innovation and setting new standards of excellence in the translation industry. The insights gleaned from this workshop are one more step towards shaping the future of translation workflows and compensation models, paving the way for a more efficient, inclusive, and equitable translation landscape. It may serve as an example of the power of collaboration between humans and AI in advancing translation practices and fostering professional growth within the industry.