Search Results for author:
Found 24 papers, 8 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.
Going beyond zero-shot MT: combining phonological, morphological and semantic factors. The UdS-DFKI System at IWSLT 2017
This paper describes the UdS-DFKI participation to the multilingual task of the IWSLT Evaluation 2017.
The Effect of Domain and Diacritics in Yoruba–English Neural Machine Translation
Massively multilingual machine translation (MT) has shown impressive capabilities and including zero and few-shot translation between low-resource language pairs.
Tracing Source Language Interference in Translation with Graph-Isomorphism Measures
Previous research has used linguistic features to show that translations exhibit traces of source language interference and that phylogenetic trees between languages can be reconstructed from the results of translations into the same language.
AVASAG: A German Sign Language Translation System for Public Services (short paper)
This paper presents an overview of AVASAG; an ongoing applied-research project developing a text-to-sign-language translation system for public services.
AFRIDOC-MT: Document-level MT Corpus for African Languages
This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, Hausa, Swahili, Yor\`ub\'a, and Zulu.
Translating away Translationese without Parallel Data
We show how we can eliminate the need for parallel validation data by combining the self-supervised loss with an unsupervised loss.
Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper
Traditional media typically adopt an editorial line that can be used by their potential readers as an indicator of the media bias.
When your Cousin has the Right Connections: Unsupervised Bilingual Lexicon Induction for Related Data-Imbalanced Languages
Most existing approaches for unsupervised bilingual lexicon induction (BLI) depend on good quality static or contextual embeddings requiring large monolingual corpora for both languages.
Explaining Translationese: why are Neural Classifiers Better and what do they Learn?
Previous research did not show $(i)$ whether the difference is because of the features, the classifiers or both, and $(ii)$ what the neural classifiers actually learn.
Exploiting Social Media Content for Self-Supervised Style Transfer
Recent research on style transfer takes inspiration from unsupervised neural machine translation (UNMT), learning from large amounts of non-parallel data by exploiting cycle consistency loss, back-translation, and denoising autoencoders.
Towards Debiasing Translation Artifacts
Cross-lingual natural language processing relies on translation, either by humans or machines, at different levels, from translating training data to translating test sets.
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification
Traditional hand-crafted linguistically-informed features have often been used for distinguishing between translated and original non-translated texts.
The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation
We investigate how and when this training condition affects the final quality and intelligibility of a translation.
Tailoring and Evaluating the Wikipedia for in-Domain Comparable Corpora Extraction
Our best metric for domainness shows a strong correlation with the human-judged precision, representing a reasonable automatic alternative to assess the quality of domain-specific corpora.
Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation
Self-supervised neural machine translation (SSNMT) jointly learns to identify and select suitable training data from comparable (rather than parallel) corpora and to translate, in a way that the two tasks support each other in a virtuous circle.
GeBioToolkit: Automatic Extraction of Gender-Balanced Multilingual Corpus of Wikipedia Biographies
We introduce GeBioToolkit, a tool for extracting multilingual parallel corpora at sentence level, with document and gender information from Wikipedia biographies.
Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yorùbá and Twi
In this paper we focus on two African languages, Yor\`ub\'a and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing.
Analysing Coreference in Transformer Outputs
We analyse coreference phenomena in three neural machine translation systems trained with different data settings with or without access to explicit intra- and cross-sentential anaphoric information.
Self-Induced Curriculum Learning in Neural Machine Translation
Self-supervised neural machine translation (SS-NMT) learns how to extract/select suitable training data from comparable (rather than parallel) corpora and how to translate, in a way that the two tasks support each other in a virtuous circle.
An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification
First, we systematically study the NMT context vectors, i. e. output of the encoder, and their power as an interlingua representation of a sentence.
Resolving Out-of-Vocabulary Words with Bilingual Embeddings in Machine Translation
Out-of-vocabulary words account for a large proportion of errors in machine translation systems, especially when the system is used on a different domain than the one where it was trained.
