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Found 29 papers, 7 papers with code
Grapheme-Based Cross-Language Forced Alignment: Results with Uralic Languages
Forced alignment is an effective process to speed up linguistic research.
Semiautomatic Speech Alignment for Under-Resourced Languages
Cross-language forced alignment is a solution for linguists who create speech corpora for very low-resource languages.
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 Helsinki submission to the AmericasNLP shared task
The University of Helsinki participated in the AmericasNLP shared task for all ten language pairs.
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.
Controlling the Imprint of Passivization and Negation in Contextualized Representations
Contextualized word representations encode rich information about syntax and semantics, alongside specificities of each context of use.
Boosting Neural Machine Translation from Finnish to Northern Sámi with Rule-Based Backtranslation
Evaluating the results on an in-domain test set and a small out-of-domain set, we find that the RBMT backtranslation outperforms NMT backtranslation clearly for the out-of-domain test set, but also slightly for the in-domain data, for which the NMT backtranslation model provided clearly better BLEU scores than the RBMT.
Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging
This paper introduces Bayesian uncertainty modeling using Stochastic Weight Averaging-Gaussian (SWAG) in Natural Language Understanding (NLU) tasks.
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.
FinChat: Corpus and evaluation setup for Finnish chat conversations on everyday topics
Using this corpus, we also construct a retrieval-based evaluation task for Finnish chatbot development.
Effects of Language Relatedness for Cross-lingual Transfer Learning in Character-Based Language Models
We, however, show that for character-based NNLMs, only pretraining with a related language improves the ASR performance, and using an unrelated language may deteriorate it.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
OpusFilter: A Configurable Parallel Corpus Filtering Toolbox
We demonstrate the effectiveness of OpusFilter on the example of a Finnish-English news translation task based on noisy web-crawled training data.
Subword RNNLM Approximations for Out-Of-Vocabulary Keyword Search
On these tasks, interpolating the baseline RNNLM approximation and a conventional LM outperforms the conventional LM in terms of the Maximum Term Weighted Value for single-character subwords.
OpusTools and Parallel Corpus Diagnostics
We show the use of these tools in parallel corpus creation and data diagnostics.
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.
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
The University of Helsinki Submissions to the WMT19 Similar Language Translation Task
This paper describes the University of Helsinki Language Technology group{'}s participation in the WMT 2019 similar language translation task.
The University of Helsinki submissions to the WMT19 news translation task
In this paper, we present the University of Helsinki submissions to the WMT 2019 shared task on news translation in three language pairs: English-German, English-Finnish and Finnish-English.
Cognate-aware morphological segmentation for multilingual neural translation
This article describes the Aalto University entry to the WMT18 News Translation Shared Task.
Automatic Speech Recognition with Very Large Conversational Finnish and Estonian Vocabularies
Today, the vocabulary size for language models in large vocabulary speech recognition is typically several hundreds of thousands of words.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
