Approaching English-Polish Machine Translation Quality Assessment with Neural-based Methods
Not on the map. Why?
Abstract (arXiv)
This paper presents our contribution to the PolEval 2021 Task 2: Evaluation of translation quality assessment metrics. We describe experiments with pre-trained language models and state-of-the-art frameworks for translation quality assessment in both nonblind and blind versions of the task. Our solutions ranked second in the nonblind version and third in the blind version.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 22 Sep 2022.
Builds on
8 of 19 references.
References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
- 2020COMET: A Neural Framework for MT Evaluationcited by 1,024
- 2018Marian: Fast Neural Machine Translation in C++cited by 202
- 2020TransQuest: Translation Quality Estimation with Cross-lingual Transformerscited by 59
- 2021HerBERT: Efficiently Pretrained Transformer-based Language Model for Polishcited by 51
Built on by
Not cited yet.