Let’s FACE it. Finnish Poetry Generation with Aesthetics and Framing

Hämäläinen, Mika; Alnajjar, Khalid

doi:10.18653/v1/w19-8637

Cited by 12 publications

(9 citation statements)

References 25 publications

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“…Word embeddings are a vector representation of words, which are built based on the surrounding context of the word. Semantic similarity between words captured in the word embeddings can be measured using cosine similarity, which can then be utilized to cluster meanings in text [17]. Common usages for word embeddings is to acquire semantically similar words to an input word.…”

Section: Word Embeddings Of Resource-rich Languagesmentioning

confidence: 99%

When Word Embeddings Become Endangered

Alnajjar¹

2021

Multilingual Facilitation

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Big languages such as English and Finnish have many natural language processing (NLP) resources and models, but this is not the case for low-resourced and endangered languages as such resources are so scarce despite the great advantages they would provide for the language communities. The most common types of resources available for low-resourced and endangered languages are translation dictionaries and universal dependencies. In this paper, we present a method for constructing word embeddings for endangered languages using existing word embeddings of different resource-rich languages and the translation dictionaries of resource-poor languages. Thereafter, the embeddings are fine-tuned using the sentences in the universal dependencies and aligned to match the semantic spaces of the big languages; resulting in cross-lingual embeddings. The endangered languages we work with here are Erzya, Moksha, Komi-Zyrian and Skolt Sami. Furthermore, we build a universal sentiment analysis model for all the languages that are part of this study, whether endangered or not, by utilizing cross-lingual word embeddings. The evaluation conducted shows that our word embeddings for endangered languages are well-aligned with the resource-rich languages, and they are suitable for training task-specific models as demonstrated by our sentiment analysis models which achieved high accuracies. All our cross-lingual word embeddings and sentiment analysis models will be released openly via an easy-to-use Python library.

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Section: Word Embeddings Of Resource-rich Languagesmentioning

confidence: 99%

When Word Embeddings Become Endangered

Alnajjar¹

2021

Multilingual Facilitation

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“…Rule-based morphology has also been developed for many endangered languages such as the Mordvinic languages , Plains Cree (Snoek et al, 2014), Skolt Sami , Tatar (Salimzyanov et al, 2013) and Kven (Trosterud et al, 2017). Rule-based morphology can also play an important role in higher level natural language generation tasks Hämäläinen & Alnajjar, 2019b) Syntax and disambiguation have also been tackled with rule-based methods for several endangered languages (Uí Dhonnchadha & van Genabith 2006;Trosterud, 2009). More recently, there have been efforts for using rule-based methods together with neural networks to achieve the same goal (Ens et al, 2019;Hämäläinen & Wiechetek, 2020).…”

Section: Related Workmentioning

confidence: 99%

Prerequisites for Shallow-Transfer Machine Translation of Mordvin Languages: Language Documentation with a Purpose

Rueter

Hämäläinen

2021

Preprint

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This paper presents the current lexical, morphological, syntactic and rule-based machine translation work for Erzya and Moksha that can and should be used in the development of a roadmap for Mordvin linguistic research. We seek to illustrate and outline initial problem types to be encountered in the construction of an Apertium-based shallow-transfer machine translation system for the Mordvin language forms. We indicate reference points within Mordvin Studies and other parts of Uralic studies, as a point of departure for outlining a linguistic studies with a means for measuring its own progress and developing a roadmap for further studies.

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“…For example, to ensure rhyming, Lau et al (2018) exploit the fact that their data (sonnets) has a particular structure from which they can infer that certain word pairs must rhyme, which they incorporate in the modelling. Hämäläinen and Alnajjar (2019) define rules for style features. Agarwal and Kann (2020) tell the model when a rhyming word is required and then modify the prediction process.…”

Section: Poetry Generationmentioning

confidence: 99%

End-to-end style-conditioned poetry generation: What does it take to learn from examples alone?

Wöckener¹,

Haider²,

Miller³

et al. 2021

Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities An

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In this work, we design an end-to-end model for poetry generation based on conditioned recurrent neural network (RNN) language models whose goal is to learn stylistic features (poem length, sentiment, alliteration, and rhyming) from examples alone. We show this model successfully learns the 'meaning' of length and sentiment, as we can control it to generate longer or shorter as well as more positive or more negative poems. However, the model does not grasp sound phenomena like alliteration and rhyming, but instead exploits lowlevel statistical cues. Possible reasons include the size of the training data, the relatively low frequency and difficulty of these sublexical phenomena as well as model biases. We show that more recent GPT-2 models also have problems learning sublexical phenomena such as rhyming from examples alone.

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Let’s FACE it. Finnish Poetry Generation with Aesthetics and Framing

Cited by 12 publications

References 25 publications

When Word Embeddings Become Endangered

When Word Embeddings Become Endangered

Prerequisites for Shallow-Transfer Machine Translation of Mordvin Languages: Language Documentation with a Purpose

End-to-end style-conditioned poetry generation: What does it take to learn from examples alone?

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