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Found 11 papers, 2 papers with code
nEYron: Implementation and Deployment of an MT System for a Large Audit & Consulting Corporation
This paper reports on the implementation and deployment of an MT system in the Polish branch of EY Global Limited.
POLENG MT: An Adaptive MT Platform
We introduce POLENG MT, an MT platform that may be used as a cloud web application or as an on-site solution.
Neural Machine Translation with Inflected Lexicon
The paper presents experiments in neural machine translation with lexical constraints into a morphologically rich language.
Neural Translator Designed to Protect the Eastern Border of the European Union
To this end, a method based on constrained decoding that incorporates an inflected lexicon into a neural translation process was applied in the engine.
Adam Mickiewicz University’s English-Hausa Submissions to the WMT 2021 News Translation Task
This paper presents the Adam Mickiewicz University’s (AMU) submissions to the WMT 2021 News Translation Task.
Exploring the Use of Foundation Models for Named Entity Recognition and Lemmatization Tasks in Slavic Languages
This paper describes Adam Mickiewicz University's (AMU) solution for the 4th Shared Task on SlavNER.
Approaching English-Polish Machine Translation Quality Assessment with Neural-based Methods
This paper presents our contribution to the PolEval 2021 Task 2: Evaluation of translation quality assessment metrics.
Adam Mickiewicz University at WMT 2022: NER-Assisted and Quality-Aware Neural Machine Translation
One of the models in the ensemble is a document-level model, trained on parallel and synthetic longer sequences.
Self-supervised learning -- A way to minimize time and effort for precision agriculture?
Machine learning, satellites or local sensors are key factors for a sustainable and resource-saving optimisation of agriculture and proved its values for the management of agricultural land.
Detection of Criminal Texts for the Polish State Border Guard
We share this dataset and create a new task for the detection of criminal texts using the Gonito platform as the benchmark.
Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model
Cloud removal is a relevant topic in Remote Sensing as it fosters the usability of high-resolution optical images for Earth monitoring and study.
