Remove tag cross-lingual
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ACL 2021 Highlights

Sebastian Ruder

Cross-lingual transfer and multilingual NLP Beyond machine translation, I enjoyed the following papers on cross-lingual transfer and multilingual NLP: COSY: COunterfactual SYntax for Cross-Lingual Understanding. They find that incorporating dependencies is more effective than using POS tags.

NLP 52
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Retain original PDF formatting to view translated documents with Amazon Textract, Amazon Translate, and PDFBox

AWS Machine Learning Blog

Many businesses have diverse global users and need to translate text to enable cross-lingual communication between them. Amazon Translate allows tag modifications, which allows you to specify what text should not be translated. This is a manual, slow, and expensive human effort.

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Google at EMNLP 2022

Google Research AI blog

Committees Organizing Committee includes: Eunsol Choi , Imed Zitouni Senior Program Committee includes: Don Metzler , Eunsol Choi , Bernd Bohnet , Slav Petrov , Kenthon Lee Papers Transforming Sequence Tagging Into A Seq2Seq Task Karthik Raman , Iftekhar Naim , Jiecao Chen , Kazuma Hashimoto , Kiran Yalasangi , Krishna Srinivasan On the Limitations (..)

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AI for Universal Audio Understanding: Qwen-Audio Explained

AssemblyAI

For example, the model can seamlessly convert a dialogue from Mandarin to English, facilitating cross-lingual communication. Alternatively, an analysis tag indicates other types of audio processing, ensuring that the model can differentiate between direct transcription and broader audio analysis.

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The State of Multilingual AI

Sebastian Ruder

Cross-lingual performance prediction [42] could be used to estimate performance for a broader set of languages. Cross-lingual parameter-efficient transfer learning is not restricted to adapters but can take other forms [140] such as sparse sub-networks [141]. Unsupervised Cross-lingual Representation Learning at Scale.

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DrBenchmark: The First-Ever Publicly Available French Biomedical Large Language Understanding Benchmark

Marktechpost

This benchmark comprises 20 diversified tasks, including named-entity recognition, part-of-speech tagging, question-answering, semantic textual similarity, and classification. The MLMs include French generalist models, cross-lingual generalist models, French biomedical models, and an English biomedical model.

NLP 105
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Building a Sentiment Classification System With BERT Embeddings: Lessons Learned

The MLOps Blog

This model can produce language-agnostic cross-lingual sentence embeddings for 109 languages. For example, “I love being ignored” may be tagged as a negative example, and “I can be very ambitious” can be tagged as a positive example. This data is then used to train models and make inferences.

BERT 52