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Found 34 papers, 11 papers with code
The LMU Munich System for the WMT 2021 Large-Scale Multilingual Machine Translation Shared Task
This paper describes the submission of LMU Munich to the WMT 2021 multilingual machine translation task for small track #1, which studies translation between 6 languages (Croatian, Hungarian, Estonian, Serbian, Macedonian, English) in 30 directions.
Why don’t people use character-level machine translation?
We present a literature and empirical survey that critically assesses the state of the art in character-level modeling for machine translation (MT).
The LMU Munich Systems for the WMT21 Unsupervised and Very Low-Resource Translation Task
We present our submissions to the WMT21 shared task in Unsupervised and Very Low Resource machine translation between German and Upper Sorbian, German and Lower Sorbian, and Russian and Chuvash.
Findings of the WMT 2021 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT
We present the findings of the WMT2021 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT.
The LMU Munich System for the WMT20 Very Low Resource Supervised MT Task
We present our systems for the WMT20 Very Low Resource MT Task for translation between German and Upper Sorbian.
Charles Translator: A Machine Translation System between Ukrainian and Czech
We present Charles Translator, a machine translation system between Ukrainian and Czech, developed as part of a society-wide effort to mitigate the impact of the Russian-Ukrainian war on individuals and society.
Understanding Cross-Lingual Alignment -- A Survey
Cross-lingual alignment, the meaningful similarity of representations across languages in multilingual language models, has been an active field of research in recent years.
How Gender Interacts with Political Values: A Case Study on Czech BERT Models
This case study focuses on the political biases of pre-trained encoders in Czech and compares them with a representative value survey.
Enhancing Conceptual Understanding in Multimodal Contrastive Learning through Hard Negative Samples
Current multimodal models leveraging contrastive learning often face limitations in developing fine-grained conceptual understanding.
CUNI Submission to MRL 2023 Shared Task on Multi-lingual Multi-task Information Retrieval
The goal of the shared task was to develop systems for named entity recognition and question answering in several under-represented languages.
A Dataset and Strong Baselines for Classification of Czech News Texts
Pre-trained models for Czech Natural Language Processing are often evaluated on purely linguistic tasks (POS tagging, parsing, NER) and relatively simple classification tasks such as sentiment classification or article classification from a single news source.
Exploring Anisotropy and Outliers in Multilingual Language Models for Cross-Lingual Semantic Sentence Similarity
We investigate outlier dimensions and their relationship to anisotropy in multiple pre-trained multilingual language models.
Is a Prestigious Job the same as a Prestigious Country? A Case Study on Multilingual Sentence Embeddings and European Countries
We study how multilingual sentence representations capture European countries and occupations and how this differs across European languages.
Probing the Role of Positional Information in Vision-Language Models
Our results thus highlight an important issue of multimodal modeling: the mere presence of information detectable by a probing classifier is not a guarantee that the information is available in a cross-modal setup.
CUNI Systems for the WMT22 Czech-Ukrainian Translation Task
We present Charles University submissions to the WMT22 General Translation Shared Task on Czech-Ukrainian and Ukrainian-Czech machine translation.
Speaking Multiple Languages Affects the Moral Bias of Language Models
Do the models capture moral norms from English and impose them on other languages?
Do Multilingual Language Models Capture Differing Moral Norms?
Massively multilingual sentence representations are trained on large corpora of uncurated data, with a very imbalanced proportion of languages included in the training.
Combining Static and Contextualised Multilingual Embeddings
We combine the strengths of static and contextual models to improve multilingual representations.
Improving Both Domain Robustness and Domain Adaptability in Machine Translation
First, we want to reach domain robustness, i. e., we want to reach high quality on both domains seen in the training data and unseen domains.
Why don't people use character-level machine translation?
We present a literature and empirical survey that critically assesses the state of the art in character-level modeling for machine translation (MT).
Neural String Edit Distance
We propose the neural string edit distance model for string-pair matching and string transduction based on learnable string edit distance.
Towards Reasonably-Sized Character-Level Transformer NMT by Finetuning Subword Systems
Applying the Transformer architecture on the character level usually requires very deep architectures that are difficult and slow to train.
On the Language Neutrality of Pre-trained Multilingual Representations
Multilingual contextual embeddings, such as multilingual BERT and XLM-RoBERTa, have proved useful for many multi-lingual tasks.
Improving Fluency of Non-Autoregressive Machine Translation
Non-autoregressive (nAR) models for machine translation (MT) manifest superior decoding speed when compared to autoregressive (AR) models, at the expense of impaired fluency of their outputs.
Probing Representations Learned by Multimodal Recurrent and Transformer Models
In this paper, we present a meta-study assessing the representational quality of models where the training signal is obtained from different modalities, in particular, language modeling, image features prediction, and both textual and multimodal machine translation.
Neural Networks as Explicit Word-Based Rules
Filters of convolutional networks used in computer vision are often visualized as image patches that maximize the response of the filter.
End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification
Autoregressive decoding is the only part of sequence-to-sequence models that prevents them from massive parallelization at inference time.
CUNI System for the WMT18 Multimodal Translation Task
For our submission, we acquired both textual and multimodal additional data.
Input Combination Strategies for Multi-Source Transformer Decoder
In multi-source sequence-to-sequence tasks, the attention mechanism can be modeled in several ways.
CUNI System for the WMT17 Multimodal Translation Task
For Task 1 (multimodal translation), our best scoring system is a purely textual neural translation of the source image caption to the target language.
Attention Strategies for Multi-Source Sequence-to-Sequence Learning
Modeling attention in neural multi-source sequence-to-sequence learning remains a relatively unexplored area, despite its usefulness in tasks that incorporate multiple source languages or modalities.
CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks
Neural sequence to sequence learning recently became a very promising paradigm in machine translation, achieving competitive results with statistical phrase-based systems.
