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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
This repo contains implementation of different architectures for emotion recognition in conversations.
| Date | Stars |
|---|---|
| 2026-07-24 | 1525 |
| 2026-07-25 | 1525 |
| 2026-07-28 | 1525 |
| 2026-07-30 | 1525 |
| 2026-08-06 | 1525 |
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# Emotion Recognition in Conversations ## Note ``` For those enquiring about how to extract visual and audio features, please check this out: https://github.com/soujanyaporia/MUStARD``` ## Updates 🔥 🔥 🔥 | Date | Announcements | |- |- | | 10/03/2024 | If you are interested in IQ testing LLMs, check out our new work: [AlgoPuzzleVQA](https://github.com/declare-lab/puzzle-reasoning) | 03/08/2021 | 🎆 🎆 We have released a new dataset M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations. Check it out: [M2H2](https://github.com/declare-lab/M2H2-dataset). The baselines for the M2H2 dataset are created based on DialogueRNN and bcLSTM. | | 18/05/2021 | 🎆 🎆 We have released a new repo containing models to solve the problem of emotion cause recognition in conversations. Check it out: [emotion-cause-extraction](https://github.com/declare-lab/conv-emotion/tree/master/emotion-cause-extraction). Thanks to [Pengfei Hong](https://www.pengfei-hong.com/) for compiling this. | | 24/12/2020 | 🎆 🎆 Interested in the topic of recognizing emotion causes in conversations? We have just released a dataset for this. Head over to [https://github.com/declare-lab/RECCON](https://github.com/declare-lab/RECCON). | | 06/10/2020 | 🎆 🎆 New paper and SOTA in Emotion Recognition in Conversations. Refer to the directory [COSMIC](./COSMIC) for the code. Read the paper -- [COSMIC: COmmonSense knowledge for eMotion Identification in Conversations](https://arxiv.org/pdf/2010.02795.pdf). | | 30/09/2020 | New paper and baselines in utterance-level dialogue understanding have been released. Read our paper [Utterance-level Dialogue Understanding: An Empirical Study](https://arxiv.org/pdf/2009.13902.pdf). Fork the [codes](https://github.com/declare-lab/dialogue-understanding). | | 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-emotion-recognition-in-conversations | | 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. | --------------------------------------------------------------------------- COSMIC is the best performing model in this repo and please visit the links below to compare the models on different ERC datasets. [](https://paperswithcode.com/sota/emotion-recognition-in-conversation-on-4?p=cosmic-commonsense-knowledge-for-emotion) [](https://paperswithcode.com/sota/emotion-recognition-in-conversation-on-meld?p=cosmic-commonsense-knowledge-for-emotion) [](https://paperswithcode.com/sota/emotion-recognition-in-conversation-on-3?p=cosmic-commonsense-knowledge-for-emotion) [
Soujanya Poria · Nanyang Technological University · Singapore
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Navonil Majumder
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c01bf033c14abaa0, topic:natural-language-processing, topic:sentiment-analysis
matched fp:c01bf033c14abaa0, topic:pytorch