Current state-of-the-art Quality Estimation (QE) in machine translation relies on massive, proprietary LLMs, raising data privacy concerns. We demonstrate that smaller, open-source LLMs (<30B parameters) are a viable, cost-effective and privacy-preserving alternative. Using a single-pass prompting strategy, our models simultaneously generate quality scores, MQM error annotations, suggested error corrections, and full post-editions. Our analysis shows these models achieve highly competitive system-level correlations with human judgments that outperform traditional neural metrics, fine-tuned models, and human inter-annotator agreement, effectively approximating the capabilities of much larger proprietary LLMs.

Healthcare professionals are often overworked, which may impair their efficacy.Text search engines may facilitate their work.However, before making health decisions, it is important for a medical professional to consult verified sources rather than unknown web pages.In this work, we present our approach for creating a text search engine based on verified resources in the Polish language, dedicated to medical workers.This consists of collecting and comprehensively analyzing texts annotated by medical professionals and evaluating various neural reranking models.During the annotation process, we differentiate between an abstract information need and a search query.Our study shows that even within a group of trained medical specialists there is extensive disagreement on the relevance of a document to the information need.We prove that available multilingual rerankers trained in the zero-shot setup are effective for the Polish language in searches initiated by both natural language expressions and keyword search queries.

Multimodal models, which combine visual and textual information, have recently gained significant recognition.This paper addresses the multimodal challenge of Text-Image retrieval and introduces a novel task that extends the modalities to include temporal data.The Temporal Image Caption Retrieval Competition (TICRC) presented in this paper is based on the Chronicling America and Challenging America projects, which offer access to an extensive collection of digitized historic American newspapers spanning 274 years.In addition to the competition results, we provide an analysis of the delivered dataset and the process of its creation.

We introduce POLENG MT, an MT platform that may be used as a cloud web application or as an on-site solution. The platform is capable of providing accurate document translation, including the transfer of document formatting between the input document and the output document. The main feature of the on-site version is dedicated customer adaptation, which consists of training on specialized texts and applying forced terminology translation according to the user’s needs.

This paper reports on the implementation and deployment of an MT system in the Polish branch of EY Global Limited. The system supports standard CAT and MT functionalities such as translation memory fuzzy search, document translation and post-editing, and meets less common, customer-specific expectations. The deployment began in August 2018 with a Proof of Concept, and ended with the signing of the Final Version acceptance certificate in October 2021. We present the challenges that were faced during the deployment, particularly in relation to the security check and installation processes in the production environment.

The paper presents experiments in neural machine translation with lexical constraints into a morphologically rich language. In particular and we introduce a method and based on constrained decoding and which handles the inflected forms of lexical entries and does not require any modification to the training data or model architecture. To evaluate its effectiveness and we carry out experiments in two different scenarios: general and domain-specific. We compare our method with baseline translation and i.e. translation without lexical constraints and in terms of translation speed and translation quality. To evaluate how well the method handles the constraints and we propose new evaluation metrics which take into account the presence and placement and duplication and inflectional correctness of lexical terms in the output sentence.

This paper describes the automatic summarization system developed for the Polish language. The system implements sentence-based extractive summarization technique, which consists in determining most important sentences in document due to their computed salience. A structure of the system is presented, as well as the evaluation method and achieved results. The presented attempt is intended to serve as the baseline for future solutions, as it is the first summarization project evaluated against the Polish Summaries Corpus, the standardized corpus of summaries for the Polish language.

This paper describes a foray into teaching internationalization by attempting to do a collaborative project between students in the United States and Poland. The project required Polish students to work with software developed by American students and to provide feedback to the Americans on how easy it was to understand and modify their code. Students communicated via email and online chats as well as in a live session facilitated by Google Hangout. The goals were to get students in both countries to appreciate the clarity needed to communicate and work with international colleagues and to have them experience the myriad issues involved in such collaborations. We report the details of the project we assigned, the processes we went through to set up the collaboration, and our successes and failures as we worked toward our goals.