A Small Griko-Italian Speech Translation Corpus

Boito, Marcely Zanon; Anastasopoulos, Antonios; Villavicencio, Aline; Besacier, Laurent; Lekakou, Marika

doi:10.21437/sltu.2018-8

Cited by 6 publications

(6 citation statements)

References 31 publications

(46 reference statements)

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“…Among severely endangered varieties, Griko is the most represented in NLP. Previous work includes two Griko-Italian parallel corpora: A corpus of narratives with POS annotations and a small speech-derived corpus annotated with morphosyntactic, POS, glosses, and speech-related information (Boito et al, 2018;Lekakou et al, 2013). Other efforts in this space include Molise Slavic, for which field recordings, transcriptions, and Italian and German translations have been made available for the varieties of Acquaviva Collecroce, San Felice, and Montemitro (Breu, 2017).…”

Section: Nlp For Specific Varieties Of Italymentioning

confidence: 99%

Language Varieties of Italy: Technology Challenges and Opportunities

Ramponi

2024

Transactions of the Association for Computational Linguistics

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Italy is characterized by a one-of-a-kind linguistic diversity landscape in Europe, which implicitly encodes local knowledge, cultural traditions, artistic expressions, and history of its speakers. However, most local languages and dialects in Italy are at risk of disappearing within a few generations. The NLP community has recently begun to engage with endangered languages, including those of Italy. Yet, most efforts assume that these varieties are under-resourced language monoliths with an established written form and homogeneous functions and needs, and thus highly interchangeable with each other and with high-resource, standardized languages. In this paper, we introduce the linguistic context of Italy and challenge the default machine-centric assumptions of NLP for Italy’s language varieties. We advocate for a shift in the paradigm from machine-centric to speaker-centric NLP, and provide recommendations and opportunities for work that prioritizes languages and their speakers over technological advances. To facilitate the process, we finally propose building a local community towards responsible, participatory efforts aimed at supporting vitality of languages and dialects of Italy.

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Section: Nlp For Specific Varieties Of Italymentioning

confidence: 99%

Language Varieties of Italy: Technology Challenges and Opportunities

Ramponi

2024

Transactions of the Association for Computational Linguistics

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“…We simulate the low-resource scenario by giving the system access to a subset of the data with the possible addition of noise (e.g., Besacier, Zhou, and Gao 2006;Stahlberg et al 2016). Some have compiled small corpora in the course of their work, for example, Griko (Boito et al 2018) or Mboshi (Godard et al 2018a;Rialland et al 2018). Some collaborations have tapped a long-standing collection activity by one of the partners, for example, Yongning Na (Adams et al 2017).…”

Section: Test Sets and Evaluation Measuresmentioning

confidence: 99%

Sparse Transcription

Bird

2021

Computational Linguistics

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The transcription bottleneck is often cited as a major obstacle for efforts to document the world's endangered languages and supply them with language technologies. One solution is to extend methods from automatic speech recognition and machine translation, and recruit linguists to provide narrow phonetic transcriptions and sentence-aligned translations. However, I believe that these approaches are not a good fit with the available data and skills, or with long-established practices that are essentially word based. In seeking a more effective approach, I consider a century of transcription practice and a wide range of computational approaches, before proposing a computational model based on spoken term detection which I call “sparse transcription.” This represents a shift away from current assumptions that we transcribe phones, transcribe fully, and transcribe first. Instead, sparse transcription combines the older practice of word-level transcription with interpretive, iterative, and interactive processes which are amenable to wider participation and which open the way to new methods for processing oral languages.

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“…There is no large-scale monolingual corpus of Griko, but there are two Griko-Italian parallel corpora (Zanon Boito et al, 2018;, with the smaller one including gold word alignment annotations. However, Griko has never had a consistent orthography, and hence its tokenisation and word segmentation differ across these corpora: the smaller data set is based on orthographic conventions from Italian, while the larger one follows the concept of a phonological word .…”

Section: Grikomentioning

confidence: 99%

“…As with Na, there is no large-scale monolingual data to pretrain with. As cross-lingual resources for Griko, there are two parallel corpora that are both aligned with Italian: one contains 330 sentence pairs with gold word alignment annotations 11 (Zanon Boito et al, 2018), and the second contains about 10k sentence pairs without word alignments 12 . However, since Griko has never had a consistent orthography, tokenisation and word segmentation differ across these corpora : the smaller data set is based on orthographic con-ventions from Italian, while the larger data set follows the concept of a phonological word.…”

Section: Grikomentioning

confidence: 99%

Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora

Wada¹,

Iwata²,

Matsumoto³

et al. 2021

Proceedings of the 1st Workshop on Multilingual Representation Learning

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We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus (e.g. a few hundred sentence pairs). Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence. Through sharing model parameters among different languages, our model jointly trains the word embeddings in a common cross-lingual space. We also propose to combine word and subword embeddings to make use of orthographic similarities across different languages. We base our experiments on real-world data from endangered languages, namely Yongning Na, Shipibo-Konibo, and Griko. Our experiments on bilingual lexicon induction and word alignment tasks show that our model outperforms existing methods by a large margin for most language pairs. These results demonstrate that, contrary to common belief, an encoder-decoder translation model is beneficial for learning crosslingual representations even in extremely lowresource conditions. Furthermore, our model also works well on high-resource conditions, achieving state-of-the-art performance on a German-English word-alignment task. 1

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A Small Griko-Italian Speech Translation Corpus

Cited by 6 publications

References 31 publications

Language Varieties of Italy: Technology Challenges and Opportunities

Language Varieties of Italy: Technology Challenges and Opportunities

Sparse Transcription

Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora

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