2 citations · 4 across the 6 of their papers we have counts for
7 papers
Isotropy, Clusters, and Classifiers
Timothee Mickus, Stig-Arne Grönroos, Joseph Attieh
Whether embedding spaces use all their dimensions equally, i.e., whether they are isotropic, has been a recent subject of discussion. Evidence has been accrued both for and against…
MorphPiece : A Linguistic Tokenizer for Large Language Models
Haris Jabbar
Tokenization is a critical part of modern NLP pipelines. However, contemporary tokenizers for Large Language Models are based on statistical analysis of text corpora, without much…
Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation
Stig-Arne Grönroos, Sami Virpioja, Mikko Kurimo
There are several approaches for improving neural machine translation for low-resource languages: Monolingual data can be exploited via pretraining or data augmentation; Parallel c…
Finnish Language Modeling with Deep Transformer Models
Abhilash Jain, Aku Ruohe, Stig-Arne Grönroos +1
Transformers have recently taken the center stage in language modeling after LSTM's were considered the dominant model architecture for a long time. In this project, we investigate…
The MeMAD Submission to the IWSLT 2018 Speech Translation Task
Umut Sulubacak, Jörg Tiedemann, Aku Rouhe +2
This paper describes the MeMAD project entry to the IWSLT Speech Translation Shared Task, addressing the translation of English audio into German text. Between the pipeline and end…
The MeMAD Submission to the WMT18 Multimodal Translation Task
Stig-Arne Grönroos, Benoit Huet, Mikko Kurimo +8
This paper describes the MeMAD project entry to the WMT Multimodal Machine Translation Shared Task. We propose adapting the Transformer neural machine translation (NMT) architectur…
Cognate-aware morphological segmentation for multilingual neural translation
Stig-Arne Grönroos, Sami Virpioja, Mikko Kurimo
This article describes the Aalto University entry to the WMT18 News Translation Shared Task. We participate in the multilingual subtrack with a system trained under the constrained…