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Found 15 papers, 4 papers with code
Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components
We describe the enhancement of a multilingual NMT toolkit developed as part of the FoTran project.
Silo NLP’s Participation at WAT2022
This paper provides the system description of “Silo NLP’s” submission to the Workshop on Asian Translation (WAT2022).
Morfessor-enriched features and multilingual training for canonical morphological segmentation
Our approach is to pre-segment the input data for a neural sequence-to-sequence model with the unsupervised method.
Ranked #1 on
Morpheme Segmentaiton
on UniMorph 4.0
(f1 macro avg (subtask 2) metric)
The University of Helsinki and Aalto University submissions to the WMT 2020 news and low-resource translation tasks
This paper describes the joint participation of University of Helsinki and Aalto University to two shared tasks of WMT 2020: the news translation between Inuktitut and English and the low-resource translation between German and Upper Sorbian.
MAMMOTH: Massively Multilingual Modular Open Translation @ Helsinki
NLP in the age of monolithic large language models is approaching its limits in terms of size and information that can be handled.
Isotropy, Clusters, and Classifiers
Whether embedding spaces use all their dimensions equally, i. e., whether they are isotropic, has been a recent subject of discussion.
Democratizing Neural Machine Translation with OPUS-MT
This paper presents the OPUS ecosystem with a focus on the development of open machine translation models and tools, and their integration into end-user applications, development platforms and professional workflows.
Silo NLP's Participation at WAT2022
This paper provides the system description of "Silo NLP's" submission to the Workshop on Asian Translation (WAT2022).
Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation
There are several approaches for improving neural machine translation for low-resource languages: Monolingual data can be exploited via pretraining or data augmentation; Parallel corpora on related language pairs can be used via parameter sharing or transfer learning in multilingual models; Subword segmentation and regularization techniques can be applied to ensure high coverage of the vocabulary.
Finnish Language Modeling with Deep Transformer Models
Transformers have recently taken the center stage in language modeling after LSTM's were considered the dominant model architecture for a long time.
Morfessor EM+Prune: Improved Subword Segmentation with Expectation Maximization and Pruning
Using English, Finnish, North Sami, and Turkish data sets, we show that this approach is able to find better solutions to the optimization problem defined by the Morfessor Baseline model than its original recursive training algorithm.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Multimodal Machine Translation through Visuals and Speech
Multimodal machine translation involves drawing information from more than one modality, based on the assumption that the additional modalities will contain useful alternative views of the input data.
Ranked #4 on
Multimodal Machine Translation
on Multi30K
The MeMAD Submission to the IWSLT 2018 Speech Translation Task
In this paper, we also describe the experiments leading up to our final systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Cognate-aware morphological segmentation for multilingual neural translation
This article describes the Aalto University entry to the WMT18 News Translation Shared Task.
The MeMAD Submission to the WMT18 Multimodal Translation Task
Our experiments show that the effect of the visual features in our system is small.
