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Found 13 papers, 7 papers with code
Prompsit’s Submission to the IWSLT 2018 Low Resource Machine Translation Task
This paper presents Prompsit Language Engineering’s submission to the IWSLT 2018 Low Resource Machine Translation task.
Bicleaner at WMT 2020: Universitat d’Alacant-Prompsit’s submission to the parallel corpus filtering shared task
This paper describes the joint submission of Universitat d’Alacant and Prompsit Language Engineering to the WMT 2020 shared task on parallel corpus filtering.
A multi-source approach for Breton–French hybrid machine translation
Corpus-based approaches to machine translation (MT) have difficulties when the amount of parallel corpora to use for training is scarce, especially if the languages involved in the translation are highly inflected.
An English-Swahili parallel corpus and its use for neural machine translation in the news domain
This paper describes our approach to create a neural machine translation system to translate between English and Swahili (both directions) in the news domain, as well as the process we followed to crawl the necessary parallel corpora from the Internet.
Curated Datasets and Neural Models for Machine Translation of Informal Registers between Mayan and Spanish Vernaculars
The Mayan languages comprise a language family with an ancient history, millions of speakers, and immense cultural value, that, nevertheless, remains severely underrepresented in terms of resources and global exposure.
Non-Fluent Synthetic Target-Language Data Improve Neural Machine Translation
When the amount of parallel sentences available to train a neural machine translation is scarce, a common practice is to generate new synthetic training samples from them.
Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation
The study covers eight language pairs, different training corpus sizes, two architectures, and three types of annotation: dummy tags (with no linguistic information at all), part-of-speech tags, and morpho-syntactic description tags, which consist of part of speech and morphological features.
Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence
Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Cross-lingual neural fuzzy matching for exploiting target-language monolingual corpora in computer-aided translation
The paper presents an automatic evaluation of these techniques on four language pairs that shows that our approach can successfully exploit monolingual texts in a TM-based CAT environment, increasing the amount of useful translation proposals, and that our neural model for estimating the post-editing effort enables the combination of translation proposals obtained from monolingual corpora and from TMs in the usual way.
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach
Many DA approaches aim at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent words, thus making it closer to the true data distribution of parallel sentences.
Data Augmentation
Low-Resource Neural Machine Translation
+3
Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian
This paper presents a quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems.
Fine-grained human evaluation of neural versus phrase-based machine translation
We compare three approaches to statistical machine translation (pure phrase-based, factored phrase-based and neural) by performing a fine-grained manual evaluation via error annotation of the systems' outputs.
A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions
We aim to shed light on the strengths and weaknesses of the newly introduced neural machine translation paradigm.
