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Kathy Reid
@KathyReid@aus.social  ·  activity timestamp last week

🚨 #NLP SHARED TASK 🚨

Use Mozilla Common Voice Spontaneous #Speech datasets to train #ASR #SpeechRecognition models that work for conversational speech on 21 under-represented languages.

📆 Dataset release 1 Dec
📆 Submissions 8 Dec
💰 $USD 11k prize pool !!!

boost_requested Boosts appreciated ❤️

https://community.mozilladatacollective.com/shared-task-mozilla-common-voice-spontaneous-speech-asr?utm_source=mastodon&utm_campaign=kathysharedtask

Mozilla Data Collective

Shared Task: Mozilla Common Voice Spontaneous Speech ASR

Overview Automatic speech recognition (ASR) has come a long way – but most systems are still trained on polished, read-aloud speech. So we set out to build a model that can handle the messy, beautiful reality of spontaneous responses and languages long ignored by mainstream tech. We’re raising the standards
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Ulrike Hahn
Ulrike Hahn boosted
UKP Lab
@UKPLab@sigmoid.social  ·  activity timestamp 3 months ago

🤔 What is #NLP research 𝘳𝘦𝘢𝘭𝘭𝘺 about?
We analyzed 29k+ papers to find out! 📚🔍

📌 Our NLPContributions dataset, from the ACL Anthology, reveals what authors actually contribute—artifacts, insights, and more.

📈 Trends show a swing back towards language & society. Curious where you fit in?

🎁 Tools, data, and analysis await you:

📄 Paper: https://arxiv.org/abs/2409.19505
🌐Project: https://ukplab.github.io/acl25-nlp-contributions/
💻 Code: https://github.com/UKPLab/acl25-nlp-contributions
💾 Data: https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4678

(1/🧵)

Diagram showing a taxonomy of NLP contributions, divided into two main categories:

Artifact Contributions

Method/Model

Dataset/Corpus

Task Definition

Knowledge Contributions

Model Analysis

Database Properties

Task Insights

Linguistic Findings

Societal Insights

The structure is visualized as a hierarchical tree with "NLP Contributions" at the top, branching into the two categories, each followed by their respective subtypes represented with icons. All items are grayed out except for the category labels, which are highlighted in blue.
Diagram showing a taxonomy of NLP contributions, divided into two main categories: Artifact Contributions Method/Model Dataset/Corpus Task Definition Knowledge Contributions Model Analysis Database Properties Task Insights Linguistic Findings Societal Insights The structure is visualized as a hierarchical tree with "NLP Contributions" at the top, branching into the two categories, each followed by their respective subtypes represented with icons. All items are grayed out except for the category labels, which are highlighted in blue.
Diagram showing a taxonomy of NLP contributions, divided into two main categories: Artifact Contributions Method/Model Dataset/Corpus Task Definition Knowledge Contributions Model Analysis Database Properties Task Insights Linguistic Findings Societal Insights The structure is visualized as a hierarchical tree with "NLP Contributions" at the top, branching into the two categories, each followed by their respective subtypes represented with icons. All items are grayed out except for the category labels, which are highlighted in blue.
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UKP Lab
@UKPLab@sigmoid.social  ·  activity timestamp 3 months ago

🤔 What is #NLP research 𝘳𝘦𝘢𝘭𝘭𝘺 about?
We analyzed 29k+ papers to find out! 📚🔍

📌 Our NLPContributions dataset, from the ACL Anthology, reveals what authors actually contribute—artifacts, insights, and more.

📈 Trends show a swing back towards language & society. Curious where you fit in?

🎁 Tools, data, and analysis await you:

📄 Paper: https://arxiv.org/abs/2409.19505
🌐Project: https://ukplab.github.io/acl25-nlp-contributions/
💻 Code: https://github.com/UKPLab/acl25-nlp-contributions
💾 Data: https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4678

(1/🧵)

Diagram showing a taxonomy of NLP contributions, divided into two main categories:

Artifact Contributions

Method/Model

Dataset/Corpus

Task Definition

Knowledge Contributions

Model Analysis

Database Properties

Task Insights

Linguistic Findings

Societal Insights

The structure is visualized as a hierarchical tree with "NLP Contributions" at the top, branching into the two categories, each followed by their respective subtypes represented with icons. All items are grayed out except for the category labels, which are highlighted in blue.
Diagram showing a taxonomy of NLP contributions, divided into two main categories: Artifact Contributions Method/Model Dataset/Corpus Task Definition Knowledge Contributions Model Analysis Database Properties Task Insights Linguistic Findings Societal Insights The structure is visualized as a hierarchical tree with "NLP Contributions" at the top, branching into the two categories, each followed by their respective subtypes represented with icons. All items are grayed out except for the category labels, which are highlighted in blue.
Diagram showing a taxonomy of NLP contributions, divided into two main categories: Artifact Contributions Method/Model Dataset/Corpus Task Definition Knowledge Contributions Model Analysis Database Properties Task Insights Linguistic Findings Societal Insights The structure is visualized as a hierarchical tree with "NLP Contributions" at the top, branching into the two categories, each followed by their respective subtypes represented with icons. All items are grayed out except for the category labels, which are highlighted in blue.
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Ulrike Hahn
Ulrike Hahn boosted
Miguel Afonso Caetano
@remixtures@tldr.nettime.org  ·  activity timestamp 5 months ago

"Asking scientists to identify a paradigm shift, especially in real time, can be tricky. After all, truly ground-shifting updates in knowledge may take decades to unfold. But you don’t necessarily have to invoke the P-word to acknowledge that one field in particular — natural language processing, or NLP — has changed. A lot.

The goal of natural language processing is right there on the tin: making the unruliness of human language (the “natural” part) tractable by computers (the “processing” part). A blend of engineering and science that dates back to the 1940s, NLP gave Stephen Hawking a voice, Siri a brain and social media companies another way to target us with ads. It was also ground zero for the emergence of large language models — a technology that NLP helped to invent but whose explosive growth and transformative power still managed to take many people in the field entirely by surprise.

To put it another way: In 2019, Quanta reported on a then-groundbreaking NLP system called BERT without once using the phrase “large language model.” A mere five and a half years later, LLMs are everywhere, igniting discovery, disruption and debate in whatever scientific community they touch. But the one they touched first — for better, worse and everything in between — was natural language processing. What did that impact feel like to the people experiencing it firsthand?

Quanta interviewed 19 current and former NLP researchers to tell that story. From experts to students, tenured academics to startup founders, they describe a series of moments — dawning realizations, elated encounters and at least one “existential crisis” — that changed their world. And ours."

https://www.quantamagazine.org/when-chatgpt-broke-an-entire-field-an-oral-history-20250430/

#AI#GenerativeAI#ChatGPT#NLP#OralHistory #LLMs #Chatbots

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Miguel Afonso Caetano
@remixtures@tldr.nettime.org  ·  activity timestamp 5 months ago

"Asking scientists to identify a paradigm shift, especially in real time, can be tricky. After all, truly ground-shifting updates in knowledge may take decades to unfold. But you don’t necessarily have to invoke the P-word to acknowledge that one field in particular — natural language processing, or NLP — has changed. A lot.

The goal of natural language processing is right there on the tin: making the unruliness of human language (the “natural” part) tractable by computers (the “processing” part). A blend of engineering and science that dates back to the 1940s, NLP gave Stephen Hawking a voice, Siri a brain and social media companies another way to target us with ads. It was also ground zero for the emergence of large language models — a technology that NLP helped to invent but whose explosive growth and transformative power still managed to take many people in the field entirely by surprise.

To put it another way: In 2019, Quanta reported on a then-groundbreaking NLP system called BERT without once using the phrase “large language model.” A mere five and a half years later, LLMs are everywhere, igniting discovery, disruption and debate in whatever scientific community they touch. But the one they touched first — for better, worse and everything in between — was natural language processing. What did that impact feel like to the people experiencing it firsthand?

Quanta interviewed 19 current and former NLP researchers to tell that story. From experts to students, tenured academics to startup founders, they describe a series of moments — dawning realizations, elated encounters and at least one “existential crisis” — that changed their world. And ours."

https://www.quantamagazine.org/when-chatgpt-broke-an-entire-field-an-oral-history-20250430/

#AI#GenerativeAI#ChatGPT#NLP#OralHistory #LLMs #Chatbots

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pettter
@pettter@social.accum.se  ·  activity timestamp 9 months ago

I might as well do another #introduction specifically for the #academic side of this here fediverse:

Coming from #theoreticalCS (with applications in #NLP) to doing #digitalhumanities (computational #musicology), I've now landed in #ResponsibleAI. Specifically, I'm interested in exploring #AntiCapitalistAI, both sharpening existing critiques of current AI practise by confronting capital and exploring inherent politics of technologies, and finding better ones for a socialist world.

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