Papers by Steinþór Steingrímsson

8 papers
Language Technology Programme for Icelandic 2019-2023 (2020.lrec-1)

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Challenge: a new national language technology programme for Icelandic is described . the programme aims to make Icelandic usable in communication and interactions in the digital world .
Approach: They describe a new national language technology programme for Icelandic . the programme aims to make Icelandic usable in communication and interactions in the digital world .
Outcome: The proposed programme aims to make Icelandic usable in communication and interactions in the digital world.
Evolving Large Text Corpora: Four Versions of the Icelandic Gigaword Corpus (2022.lrec-1)

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Challenge: The Icelandic Gigaword Corpus was first published in 2018 and has since grown to include more than 50 million words.
Approach: They describe the evolution of the Icelandic Gigaword Corpus in its first four years . they show how the corpus has grown almost 50% in size from the first version to the fourth .
Outcome: The Gigaword corpus has grown 50% from its first version to its fourth version and is now available under permissive licenses.
Samrómur: Crowd-sourcing Data Collection for Icelandic Speech Recognition (2020.lrec-1)

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Challenge: Samrómur is a web application built upon Mozilla Foundation’s open-source voice collection platform Common Voice.
Approach: They describe an ongoing project of speech data collection using the web application Samrómur which is built upon Common Voice, Mozilla Foundation’s web platform for open-source voice collection.
Outcome: The proposed system will be the largest open speech corpus for Icelandic collected from the public domain.
Constructing Multimodal Language Learner Texts Using LARA: Experiences with Nine Languages (2020.lrec-1)

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Challenge: LARA is an open source project that aims to support easy conversion of plain texts into online versions suitable for use by language learners.
Approach: They propose to support easy conversion of plain texts into online versions suitable for use by language learners.
Outcome: The proposed platform is suitable for creating texts in multiple languages via crowdsourcing techniques that can be used for teaching a language via reading and listening.
Risamálheild: A Very Large Icelandic Text Corpus (L18-1)

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Challenge: The corpus contains more than one billion running words from mostly contemporary texts.
Approach: They present the Icelandic Gigaword Corpus (IGC) with minimal work and resources.
Outcome: The Icelandic Gigaword Corpus (IGC) contains more than one billion running words from mostly contemporary texts.
Effectively Aligning and Filtering Parallel Corpora under Sparse Data Conditions (2020.acl-srw)

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Challenge: Parallel corpora are key to developing good machine translation systems, but abundant parallel data is hard to come by for languages with a low number of speakers.
Approach: They propose an unsupervised alignment method that can handle rich morphology by removing incorrect translations and segments containing extraneous data.
Outcome: The proposed method maximizes the number of correctly translated segments in a corpus and minimises noise by removing incorrect translations and segments containing extraneous data.
Facilitating Corpus Usage: Making Icelandic Corpora More Accessible for Researchers and Language Users (2020.lrec-1)

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Challenge: Gigaword corpus is a large text corpus used in natural language processing . large corpora are needed to achieve better performance in the field of NLP .
Approach: They propose a set of tools to facilitate the use of the Icelandic Gigaword Corpus . they provide n-grams based on the corpus, and a variety of pre-trained word embeddings models .
Outcome: The proposed tools facilitate the use of the Icelandic Gigaword corpus in the field of Natural Language Processing and other fields.
IceBATS: An Icelandic Adaptation of the Bigger Analogy Test Set (2022.lrec-1)

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Challenge: a new test set that measures word embeddings' ability to recognize linguistic regularities is presented in a paper in elijsson, iran . the test sets are a good quality estimator for extrinsic evaluation of word embedded models .
Approach: They propose a test set that measures language models' ability to recognize linguistic regularities in a balanced way.
Outcome: The proposed set is apt at measuring the capabilities of word embedding models.

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