MELD-ST: An Emotion-aware Speech Translation Dataset

Paper: https://arxiv.org/abs/2405.13233

  Overview

This emotion-aware speech translation dataset is a multi-language dataset extracted from the TV show "Friends." It includes English, Japanese, and German subtitles along with corresponding timestamps. This dataset is designed for natural language processing tasks.

  Contents

The dataset is partitioned into train, test, and development subsets to streamline… See the full description on the dataset page: https://huggingface.co/datasets/ku-nlp/MELD-ST.

Multimodal EmotionLines Dataset (MELD) is an enhanced and extended version of the EmotionLines dataset, designed for emotion recognition in conversations. MELD contains more than 1400 dialogues and 13000 utterances from the Friends TV series. Each utterance in a dialogue is labeled with one of seven emotions: Anger, Disgust, Sadness, Joy, Neutral, Surprise, or Fear. The dataset also includes sentiment annotations (positive, negative, and neutral) for each utterance. MELD includes text, audio, and visual modalities, making it ideal for multimodal emotion recognition research.

Citation:

If you use this dataset in your research, please cite the following papers:

• S. Poria, D. Hazarika, N. Majumder, G. Naik, E. Cambria, R. Mihalcea. MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversation. ACL 2019.
• Chen, S.Y., Hsu, C.C., Kuo, C.C. and Ku, L.W. EmotionLines: An Emotion Corpus of Multi-Party Conversations. arXiv preprint arXiv:1802.08379 (2018).

Dataset Source:

The dataset is open-source and can be accessed at https://github.com/declare-lab/MELD.git.

Multimodal EmotionLines Dataset (MELD) has been created by enhancing and extending EmotionLines dataset. MELD contains the same dialogue instances available in EmotionLines, but it also encompasses audio and visual modality along with text. MELD has more than 1400 dialogues and 13000 utterances from Friends TV series. Multiple speakers participated in the dialogues. Each utterance in a dialogue has been labeled by any of these seven emotions -- Anger, Disgust, Sadness, Joy, Neutral, Surprise and Fear. MELD also has sentiment (positive, negative and neutral) annotation for each utterance.

Please cite the following papers if you use this dataset in your work.

• S. Poria, D. Hazarika, N. Majumder, G. Naik, R. Mihalcea, E. Cambria. MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversation (2018).
• Chen, S.Y., Hsu, C.C., Kuo, C.C. and Ku, L.W. EmotionLines: An Emotion Corpus of Multi-Party Conversations. arXiv preprint arXiv:1802.08379 (2018).

This dataset has been taken from here.

Experimental results of emotion inference on the MELD dataset.

https://github.com/declare-lab/conv-emotion Conversational transfer learning for emotion recognition. Hazarika, D., Poria, S., Zimmermann, R., & Mihalcea, R. (2020). Information Fusion.

26/07/2020 New DialogueGCN code has been released. Please visit https://github.com/declare-lab/conv-emotion/tree/master/DialogueGCN-mianzhang. All the credit goes to the Mian Zhang (https://github.com/mianzhang/) 11/07/2020 Interested in reading the papers on ERC or related tasks such as sarcasm detection in conversations? We have compiled a comprehensive reading list for papers. Please visit https://github.com/declare-lab/awesome-erc 07/06/2020: New state-of-the-art results for the ERC task will be released soon. 07/06/2020: The conv-emotion repo will be maintained on https://github.com/declare-lab/ 22/12/2019: Code for DialogueGCN has been released. 11/10/2019: New Paper: Conversational Transfer Learning for Emotion Recognition. 09/08/2019: New paper on Emotion Recognition in Conversation (ERC). 06/03/2019: Features and codes to train DialogueRNN on the MELD dataset have been released. 20/11/2018: End-to-end version of ICON and DialogueRNN have been released.

Description: Public dataset that was used in this study, including all measured variables. Format: pkl file. Availability: The complete dataset can be downloaded from the following link: URL: https://affective-meld.github.io/. (ZIP)

Modern dialogue systems rely on emotion recognition in conversation (ERC) as a core element enabling empathetic and human-like interactions. However, the weak correlation between emotions and semantics poses significant challenges to emotion recognition in dialogue. Semantically similar utterances can express different types of emotions, depending on the context or speaker. In order to tackle this challenge, our paper proposes a novel loss called Focal Weighted Loss (FWL) with adversarial training and the compact language model MobileBERT. Our proposed loss function handles the problem of imbalanced emotion classification through Focal Weighted Loss and adversarial training and does not require large batch sizes or more computational resources. Our approach has been employed on four text emotion recognition benchmark datasets, MELD, EmoryNLP, DailyDialog and IEMOCAP demonstrating competitive performance. Extensive experiments on these benchmark datasets validate the effectiveness of our proposed FWL with adversarial training. This enables more human-like interactions on digital platforms. Our approach shows its potential to deliver competitive performance under limited resource constraints, comparable to large language models.

Super Emotion Dataset

  Associated Paper

We provide full documentation of the dataset construction process in the accompanying paper: 📘 The Super Emotion Dataset (PDF)

  Dataset Summary

The Super Emotion dataset is a large-scale, multilabel dataset for emotion classification, aggregated from six prominent emotion datasets:

MELD GoEmotions TwitterEmotion ISEAR SemEval Crowdflower

It contains 552,821 unique text samples and 570,457 total emotion label assignments… See the full description on the dataset page: https://huggingface.co/datasets/cirimus/super-emotion.