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Found 41 papers, 14 papers with code
Findings of the 2021 Conference on Machine Translation (WMT21)
This paper presents the results of the newstranslation task, the multilingual low-resourcetranslation for Indo-European languages, thetriangular translation task, and the automaticpost-editing task organised as part of the Con-ference on Machine Translation (WMT) 2021. In the news task, participants were asked tobuild machine translation systems for any of10 language pairs, to be evaluated on test setsconsisting mainly of news stories.
CUNI Basque-to-English Submission in IWSLT18
We present our submission to the IWSLT18 Low Resource task focused on the translation from Basque-to-English.
UFAL Submissions to the IWSLT 2016 MT Track
We present our submissions to the IWSLT 2016 machine translation task, as our first attempt to translate subtitles and one of our early experiments with neural machine translation (NMT).
CUNI Submission for the Inuktitut Language in WMT News 2020
This paper describes CUNI submission to the WMT 2020 News Translation Shared Task for the low-resource scenario Inuktitut–English in both translation directions.
Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier
We introduce the Aya Expanse model family, a new generation of 8B and 32B parameter multilingual language models, aiming to address the critical challenge of developing highly performant multilingual models that match or surpass the capabilities of monolingual models.
Preliminary WMT24 Ranking of General MT Systems and LLMs
This is the preliminary ranking of WMT24 General MT systems based on automatic metrics.
AI-Assisted Human Evaluation of Machine Translation
The recently adopted annotation protocol, Error Span Annotation (ESA), has annotators marking erroneous parts of the translation and then assigning a final score.
Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translation
High-quality Machine Translation (MT) evaluation relies heavily on human judgments.
Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets
We benchmark metric performance, assess their incremental performance over successive campaigns, and measure their sensitivity to a range of linguistic phenomena.
Navigating the Metrics Maze: Reconciling Score Magnitudes and Accuracies
Ten years ago a single metric, BLEU, governed progress in machine translation research.
GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4
This paper introduces GEMBA-MQM, a GPT-based evaluation metric designed to detect translation quality errors, specifically for the quality estimation setting without the need for human reference translations.
SLIDE: Reference-free Evaluation for Machine Translation using a Sliding Document Window
This suggests that source context may provide the same information as a human reference in disambiguating source ambiguities.
Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References
Most research about natural language generation (NLG) relies on evaluation benchmarks with limited references for a sample, which may result in poor correlations with human judgements.
Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models
To further improve the performance of LLMs on MT quality assessment, we investigate several prompting designs, and propose a new prompting method called \textbf{\texttt{Error Analysis Prompting}} (EAPrompt) by combining Chain-of-Thoughts (Wei et al., 2022) and Error Analysis (Lu et al., 2023).
Large Language Models Are State-of-the-Art Evaluators of Translation Quality
We describe GEMBA, a GPT-based metric for assessment of translation quality, which works both with a reference translation and without.
Poor Man's Quality Estimation: Predicting Reference-Based MT Metrics Without the Reference
Machine translation quality estimation (QE) predicts human judgements of a translation hypothesis without seeing the reference.
Searching for a higher power in the human evaluation of MT
In MT evaluation, pairwise comparisons are conducted to identify the better system.
The Reality of Multi-Lingual Machine Translation
Machine translation is for us a prime example of deep learning applications where human skills and learning capabilities are taken as a benchmark that many try to match and surpass.
To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation
Automatic metrics are commonly used as the exclusive tool for declaring the superiority of one machine translation system's quality over another.
On User Interfaces for Large-Scale Document-Level Human Evaluation of Machine Translation Outputs
Recent studies emphasize the need of document context in human evaluation of machine translations, but little research has been done on the impact of user interfaces on annotator productivity and the reliability of assessments.
THEaiTRE 1.0: Interactive generation of theatre play scripts
We present the first version of a system for interactive generation of theatre play scripts.
Findings of the 2020 Conference on Machine Translation (WMT20)
In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories.
CUNI Systems for the Unsupervised and Very Low Resource Translation Task in WMT20
This paper presents a description of CUNI systems submitted to the WMT20 task on unsupervised and very low-resource supervised machine translation between German and Upper Sorbian.
Gender Coreference and Bias Evaluation at WMT 2020
Our work presents the largest evidence for the phenomenon in more than 19 systems submitted to the WMT over four diverse target languages: Czech, German, Polish, and Russian.
Announcing CzEng 2.0 Parallel Corpus with over 2 Gigawords
We present a new release of the Czech-English parallel corpus CzEng 2. 0 consisting of over 2 billion words (2 "gigawords") in each language.
THEaiTRE: Artificial Intelligence to Write a Theatre Play
We present THEaiTRE, a starting project aimed at automatic generation of theatre play scripts.
Exploring Benefits of Transfer Learning in Neural Machine Translation
For the former scenario, we present a proof-of-concept method by reusing a model trained by other researchers.
Efficiently Reusing Old Models Across Languages via Transfer Learning
To show the applicability of our method, we recycle a Transformer model trained by different researchers and use it to seed models for different language pairs.
CUNI Submission for Low-Resource Languages in WMT News 2019
This paper describes the CUNI submission to the WMT 2019 News Translation Shared Task for the low-resource languages: Gujarati-English and Kazakh-English.
CUNI Submissions in WMT18
Our main focus was the low-resource language pair of Estonian and English for which we utilized Finnish parallel data in a simple method.
Trivial Transfer Learning for Low-Resource Neural Machine Translation
We present a simple transfer learning method, where we first train a "parent" model for a high-resource language pair and then continue the training on a lowresource pair only by replacing the training corpus.
Low Resource Neural Machine Translation
Low-Resource Neural Machine Translation
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SubGram: Extending Skip-gram Word Representation with Substrings
Skip-gram (word2vec) is a recent method for creating vector representations of words ("distributed word representations") using a neural network.
An Exploration of Word Embedding Initialization in Deep-Learning Tasks
We support this hypothesis by observing the performance in learning lexical relations and by the fact that the network can learn to perform reasonably in its task even with fixed random embeddings.
CUNI NMT System for WAT 2017 Translation Tasks
The paper presents this year{'}s CUNI submissions to the WAT 2017 Translation Task focusing on the Japanese-English translation, namely Scientific papers subtask, Patents subtask and Newswire subtask.
Curriculum Learning and Minibatch Bucketing in Neural Machine Translation
We examine the effects of particular orderings of sentence pairs on the on-line training of neural machine translation (NMT).
LanideNN: Multilingual Language Identification on Character Window
In language identification, a common first step in natural language processing, we want to automatically determine the language of some input text.
