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Facilitating Emotional Interpretation in Language Translation

Team of global researchers unveils emotionally-labeled dataset MELD-ST. This robust dataset focuses on the emotional undertone of speech, enabling AI models to comprehend not merely the spoken words, but also the emotions expressed during conversations. Speech samples from various sources...

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Researchers from diverse global backgrounds have unveiled a novel dataset named MELD-ST. This...
Researchers from diverse global backgrounds have unveiled a novel dataset named MELD-ST. This collection is equipped with emotional labels, enabling artificial models to comprehend not only the spoken words, but also the emotional undertones in a conversation, beyond simple translation. The dataset sources its speech data from various resources.

Facilitating Emotional Interpretation in Language Translation

Emotion-Aware Dataset 'MELD-ST' for Better Translations

A team of international researchers has introduced a novel dataset named MELD-ST. This dataset, equipped with emotional tone labels, enables models to not only translate words, but also the feelings people express in a conversation. The data used for this project is derived from the popular TV show "Friends," where timestamps of each dialogue are provided, allowing models to deliver more natural translations across languages and detect emotions.

🔗 Credit: Romain Vignes

To get your hands on the MELD-ST Emotion-Aware Speech Translation Dataset — which is aimed at enhancing translation quality for English-to-Japanese and English-to-German linguistic pairs — follow these straightforward steps:

MELD-ST Dataset Highlights

  • Objective: MELD-ST is an emotion-aware dataset created for speech translation, offering approximately 10,000 utterances in each language pair, with emotion labels derived from the MELD dataset[1].
  • Possible Uses: This dataset is employed to train and assess models (such as SeamlessM4T) on emotion-conscious speech translation tasks.

Accessing or Downloading the Dataset

  1. Publication Review: Start by checking out the MELD-ST paper on OpenReview for details and access instructions[1].
  2. License Information: The dataset is available under the CC BY-SA 4.0 license, allowing for free download, modification, and distribution, provided proper attribution is given[1].
  3. Download Instructions: Scour the OpenReview page for the MELD-ST dataset, or use the provided link in the metadata[1]. Strictly adhere to the paper's guidance for downloading the dataset.
  4. Contact Authors: If a direct download link isn't provided, you can reach out to the authors listed in the publication[1].
  5. Repositories: Explore platforms like Hugging Face Datasets, Zenodo, and institutional repositories for 'MELD-ST.'

In a Nutshell

| Sequence | Action ||----------------|------------------------------------------------------|| 1. Read Publication | Familiarize yourself with MELD-ST on OpenReview[1] || 2. Check License | Verify CC BY-SA 4.0 terms[1] || 3. Follow Download Instructions | Navigate the OpenReview page or paper for downloading the dataset || 4. Contact Authors | Mail the authors listed in the publication if necessary || 5. Explore Repositories | Search platforms for 'MELD-ST' |

Pro Tips

  • Always assess the dataset license and attribution requirements before usage.
  • Refer to the latest versions of the publication for any updated download links or instructions.
  • If you cannot find a direct download, always consider reaching out to the authors.

Armed with these guidelines, you should have a smooth experience accessing or downloading the MELD-ST emotion-aware dataset, aiding your research into emotional speech translations[1].

  • By exploring the MELD-ST paper on OpenReview, you can find details and instructions for downloading the MELD-ST Emotion-Aware Speech Translation Dataset.
  • This dataset, licensed under CC BY-SA 4.0, is designed to improve translation quality for English-to-Japanese and English-to-German linguistic pairs, using artificial intelligence and data from the MELD dataset for research purposes.

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