Automatic Dialect Adaptation in Finnish and its Effect on Perceived Creativity

Hämäläinen, Mika; Partanen, Niko; Alnajjar, Khalid; Rueter, Jack; Poibeau, Thierry

doi:10.20944/preprints202009.0172.v1

Cited by 7 publications

(5 citation statements)

References 11 publications

(12 reference statements)

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“…Poetry generation by artificial intelligence has been actively studied in various languages. In addition to English and Japanese, there have been studies on French poetry generation [4] using GPT-2 [3] and Chinese poetry generation [5] using GPT-2.…”

Section: Related Workmentioning

confidence: 99%

Implementation of Autoregressive Language Models for Generation of Seasonal Fixed-form Haiku in Japanese

Hirata

Yokoyama

Yamashita

et al. 2023

IIAI Letters on Informatics and Interdisciplinary Research

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This paper describes the implementation of an artificial intelligence haiku generator. We trained language models using existing haiku and literary studies, evaluated model performance using automatically computable evaluation indices such as perplexity, and subjectively evaluated the generated haiku by using a questionnaire. The main contributions of this paper are as follows. First, the effectiveness of a series of model evaluation processes, including automatically calculable evaluation indices and the results of subjective evaluations using questionnaires, is investigated. These processes are effective in the development of haiku generation models. Second, high-quality haiku generation is achieved using high-performance language models such as GPT-2 and BART. The results of the questionnaire survey revealed that it is possible to generate sensible haiku comparable to those written by humans. The insight gained from this study is applicable to other generative tasks.

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Section: Related Workmentioning

confidence: 99%

Implementation of Autoregressive Language Models for Generation of Seasonal Fixed-form Haiku in Japanese

Hirata

Yokoyama

Yamashita

et al. 2023

IIAI Letters on Informatics and Interdisciplinary Research

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“…We use the Finnish dialect generation models presented by Hämäläinen et al (2020b) to convert standard Estonian sentences into a pseudo Estonian dialect. The dialect generation models are available through Murre Python library 8 .…”

Section: Generating Synthetic Finnish Datamentioning

confidence: 99%

Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing

2022

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The lack of resources for languages in the Americas has proven to be a problem for the creation of digital systems such as machine translation, search engines, chat bots, and more. The scarceness of digital resources for a language causes a higher impact on populations where the language is spoken by millions of people. We introduce the first official large combined corpus for deep learning of an indigenous South American low-resource language spoken by millions called Quechua. Specifically, our curated corpus is created from text gathered from the southern region of Peru where a dialect of Quechua is spoken that has not traditionally been used for digital systems as a target dialect in the past. In order to make our work repeatable by others, we also offer a public, pre-trained, BERT model called Qu-BERT which is the largest linguistic model ever trained for any Quechua type, not just the southern region dialect. We furthermore test our corpus and its corresponding BERT model on two major tasks: (1) named-entity recognition (NER) and (2) part-of-speech (POS) tagging by using state-of-the-art techniques where we achieve results comparable to other work on higher-resource languages. In this article, we describe the methodology, challenges, and results from the creation of QuBERT which is on on par with other state-of-the-art multilingual models for natural language processing achieving between 71 and 74% F1 score on NER and 84-87% on POS tasks. ReferencesWillem FH Adelaar. 2004. The languages of the Andes.

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“…The current approaches to Finnish dialect have focused on the textual modality only. Previously, bidirectional LSTM (long short-term memory) based models have been used to normalize Finnish dialects to standard Finnish (Partanen et al, 2019) and to adapt standard Finnish text into different dialectal forms (Hämäläinen et al, 2020). Similar approach has also been used to normalize historical Finnish .…”

Section: Related Workmentioning

confidence: 99%

Finnish Dialect Identification: The Effect of Audio and Text

Hämäläinen¹,

Alnajjar²,

Partanen³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Finnish is a language with multiple dialects that not only differ from each other in terms of accent (pronunciation) but also in terms of morphological forms and lexical choice. We present the first approach to automatically detect the dialect of a speaker based on a dialect transcript and transcript with audio recording in a dataset consisting of 23 different dialects. Our results show that the best accuracy is received by combining both of the modalities, as text only reaches to an overall accuracy of 57%, where as text and audio reach to 85%. Our code, models and data have been released openly on Github and Zenodo.

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Automatic Dialect Adaptation in Finnish and its Effect on Perceived Creativity

Cited by 7 publications

References 11 publications

Implementation of Autoregressive Language Models for Generation of Seasonal Fixed-form Haiku in Japanese

Implementation of Autoregressive Language Models for Generation of Seasonal Fixed-form Haiku in Japanese

Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing

Finnish Dialect Identification: The Effect of Audio and Text

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