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        <title>PFA-ERC: Psuedo-Future Augmented Dynamic Emotion Recognition in Conversations</title>
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        <namePart type="given">Tanmay</namePart>
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        <namePart type="given">Apurva</namePart>
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            <namePart type="given">Mohit</namePart>
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    <abstract>AI systems’ ability to interpret human emotions and adapt to variations is becoming more crucial as AI gets embedded into everyone’s daily lives. Emotion Recognition in Conversations (ERC) is based on this fundamental challenge. Current state-of-the-art technologies in ERC are limited due to the need for future information. We introduce High-Dimensional Temporal Fusion Transformer (HiTFT), a time-series forecasting transformer that predicts pseudo-future information to overcome this constraint. This retains the models’ dynamic nature and provides future information more efficiently than other methods. Our proposed method combines pseudo future embeddings with an encoder that models the speaker’s emotional state using past and pseudo-future information as well as inter and intra speaker interactions; these speaker states are then passed through a decoder block that predicts the inferred emotion of that utterance. We further evaluate our method and show that it achieves state of the art performance on three ERC datasets - MELD, EmoryNLP, and IEMOCap.</abstract>
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%0 Conference Proceedings
%T PFA-ERC: Psuedo-Future Augmented Dynamic Emotion Recognition in Conversations
%A Khule, Tanmay
%A Agrawal, Rishabh
%A Narayan, Apurva
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F khule-etal-2024-pfa
%X AI systems’ ability to interpret human emotions and adapt to variations is becoming more crucial as AI gets embedded into everyone’s daily lives. Emotion Recognition in Conversations (ERC) is based on this fundamental challenge. Current state-of-the-art technologies in ERC are limited due to the need for future information. We introduce High-Dimensional Temporal Fusion Transformer (HiTFT), a time-series forecasting transformer that predicts pseudo-future information to overcome this constraint. This retains the models’ dynamic nature and provides future information more efficiently than other methods. Our proposed method combines pseudo future embeddings with an encoder that models the speaker’s emotional state using past and pseudo-future information as well as inter and intra speaker interactions; these speaker states are then passed through a decoder block that predicts the inferred emotion of that utterance. We further evaluate our method and show that it achieves state of the art performance on three ERC datasets - MELD, EmoryNLP, and IEMOCap.
%U https://aclanthology.org/2024.findings-emnlp.950
%P 16196-16207

Markdown (Informal)

[PFA-ERC: Psuedo-Future Augmented Dynamic Emotion Recognition in Conversations](https://aclanthology.org/2024.findings-emnlp.950) (Khule et al., Findings 2024)

• PFA-ERC: Psuedo-Future Augmented Dynamic Emotion Recognition in Conversations (Khule et al., Findings 2024)

ACL

• Tanmay Khule, Rishabh Agrawal, and Apurva Narayan. 2024. PFA-ERC: Psuedo-Future Augmented Dynamic Emotion Recognition in Conversations. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 16196–16207, Miami, Florida, USA. Association for Computational Linguistics.