Decolonising Speech and Language Technology

Bird, Steven

doi:10.18653/v1/2020.coling-main.313

Cited by 87 publications

(106 citation statements)

References 70 publications

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“…More recent research has focused on colonising discourses in speech and language technology. For example, Bird (2020) invites us towards a postcolonial approach to computational methods for supporting language vitality, by suggesting new ways of working with indigenous communities. This includes the ability to handle regional or non-standard languages/language varieties as (predominantly) oral, emergent, untranslatable and tightly attached to a place, rather than as a communication tool whose discourse may be readily available for processing (ibid.…”

Section: Decolonising Speech and Language Technologymentioning

confidence: 99%

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

Sayers¹,

Sousa‐Silva²,

Höhn³

et al. 2021

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New language technologies are coming, thanks to the huge and competing private investment fuelling rapid progress; we can either understand and foresee their effects, or be taken by surprise and spend our time trying to catch up. This report scketches out some transformative new technologies that are likely to fundamentally change our use of language. Some of these may feel unrealistically futuristic or far-fetched, but a central purpose of this report - and the wider LITHME network - is to illustrate that these are mostly just the logical development and maturation of technologies currently in prototype. But will everyone benefit from all these shiny new gadgets? Throughout this report we emphasise a range of groups who will be disadvantaged and issues of inequality. Important issues of security and privacy will accompany new language technologies. A further caution is to re-emphasise the current limitations of AI. Looking ahead, we see many intriguing opportunities and new capabilities, but a range of other uncertainties and inequalities. New devices will enable new ways to talk, to translate, to remember, and to learn. But advances in technology will reproduce existing inequalities among those who cannot afford these devices, among the world’s smaller languages, and especially for sign language. Debates over privacy and security will flare and crackle with every new immersive gadget. We will move together into this curious new world with a mix of excitement and apprehension - reacting, debating, sharing and disagreeing as we always do. Plug in, as the human-machine era dawns.

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Section: Decolonising Speech and Language Technologymentioning

confidence: 99%

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

Sayers¹,

Sousa‐Silva²,

Höhn³

et al. 2021

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“…The discussion forum is also used to discuss weekly readings, which focus on ethics (e.g. Hovy and Spruit, 2016;Bird, 2020) and the relationship between language technology and humanities (e.g. Kuhn, 2019;Nguyen et al, 2020).…”

Section: Pedagogical Approachmentioning

confidence: 99%

Applied Language Technology: NLP for the Humanities

Hiippala¹

2021

Proceedings of the Fifth Workshop on Teaching NLP

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“…Such advances may enable better access to information as well as to products and services. However, there is a risk that such technological advances may not always be desired by the relevant communities and may indeed also cause harm to them (Bird, 2020). Moreover, cross-lingual systems in particular may exhibit biases with regard to the source language used for training and the general cultural assumptions reflected in such data.…”

Section: Broader Impactmentioning

confidence: 99%

Data Augmentation with Adversarial Training for Cross-Lingual NLI

Dong¹,

Zhu²,

Fu³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Due to recent pretrained multilingual representation models, it has become feasible to exploit labeled data from one language to train a crosslingual model that can then be applied to multiple new languages. In practice, however, we still face the problem of scarce labeled data, leading to subpar results. In this paper, we propose a novel data augmentation strategy for better cross-lingual natural language inference by enriching the data to reflect more diversity in a semantically faithful way. To this end, we propose two methods of training a generative model to induce synthesized examples, and then leverage the resulting data using an adversarial training regimen for more robustness. In a series of detailed experiments, we show that this fruitful combination leads to substantial gains in cross-lingual inference.

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Decolonising Speech and Language Technology

Cited by 87 publications

References 70 publications

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

Applied Language Technology: NLP for the Humanities

Data Augmentation with Adversarial Training for Cross-Lingual NLI

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