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NaRLE: Natural Language Models using Reinforcement Learning with Emotion Feedback [article]

Ruijie Zhou, Soham Deshmukh, Jeremiah Greer, Charles Lee
2021 arXiv   pre-print
In this paper, we focus on improving task-based conversational assistants online, primarily those working on document-type conversations (e.g., emails) whose contents may or may not be completely related  ...  Current research in dialogue systems is focused on conversational assistants working on short conversations in either task-oriented or open domain settings.  ...  Multi-label Intent Detection We consider a multi-label intent classification model of the same conversational assistant as before.  ... 
arXiv:2110.02148v1 fatcat:lxgx4g5kybhdxdxlpo4l73kmgi

Hybrid Curriculum Learning for Emotion Recognition in Conversation [article]

Lin Yang, Yi Shen, Yue Mao, Longjun Cai
2022 arXiv   pre-print
Emotion recognition in conversation (ERC) aims to detect the emotion label for each utterance.  ...  In CC, we construct a difficulty measurer based on "emotion shift" frequency within a conversation, then the conversations are scheduled in an "easy to hard" schema according to the difficulty score returned  ...  In CC, we construct a difficulty measurer based on "emotion shift" frequency within a conversation, then the conversations with lower difficulty are presented to the model before harder ones.  ... 
arXiv:2112.11718v2 fatcat:gedcq3uql5ckzc2odklcd7q4be

Hybrid Curriculum Learning for Emotion Recognition in Conversation

Lin Yang, YI Shen, Yue Mao, Longjun Cai
2022 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Emotion recognition in conversation (ERC) aims to detect the emotion label for each utterance.  ...  In CC, we construct a difficulty measurer based on "emotion shift" frequency within a conversation, then the conversations are scheduled in an "easy to hard" schema according to the difficulty score returned  ...  In CC, we construct a difficulty measurer based on "emotion shift" frequency within a conversation, then the conversations with lower difficulty are presented to the model before harder ones.  ... 
doi:10.1609/aaai.v36i10.21413 fatcat:ncxqqpqp7rfnzkrqtpwazdbydu

Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances [article]

Soujanya Poria, Navonil Majumder, Rada Mihalcea, Eduard Hovy
2019 arXiv   pre-print
Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly  ...  In this paper, we discuss these challenges and shed light on the recent research in this field.  ...  [22] , where emotion detection on conversation transcript was attempted. Recently, several works [23, 24] have devised deep learning-based techniques for ERC.  ... 
arXiv:1905.02947v1 fatcat:fmi3clkdtzd3bifn7cxipw4vim

MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations

Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, Rada Mihalcea
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
Emotion recognition in conversations (ERC) is a challenging task that has recently gained popularity due to its potential applications.  ...  We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.  ...  Conclusion In this work, we introduced MELD, a multimodal multi-party conversational emotion recognition dataset.  ... 
doi:10.18653/v1/p19-1050 dblp:conf/acl/PoriaHMNCM19 fatcat:qtbwbfyndffmdnhh35nauvpfhm

A Technical Survey on the Modeling of Topical Bot

Aishna Gupta, Anuska Rakshit
2021 American Journal of Software Engineering and Applications  
These models can identify whether the user is female/male/other, detect the change in the user's emotion during the conversation and switch the topic of discussion accordingly.  ...  Topical chats have huge data/knowledge stored in them for making the conversation interactive and engaging with humans.  ...  We learnt and came to know about so many new things related to Conversational AI, NLU and many more.  ... 
doi:10.11648/j.ajsea.20211001.12 fatcat:bb7yfm4hhrfepkmhinksvp2iey

Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances

Soujanya Poria, Navonil Majumderd, Rada Mihalceae, Eduard Hovy
2019 IEEE Access  
Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly  ...  available conversational data on platforms such as Facebook, Youtube, Reddit, Twitter, and others.  ...  [22] , where emotion detection on conversation transcript was attempted. Recently, several works [23] , [24] have devised deep learning-based techniques for ERC.  ... 
doi:10.1109/access.2019.2929050 fatcat:jqlpfqmc5bfvpmawrfhvlgiife

M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database [article]

Jinming Zhao, Tenggan Zhang, Jingwen Hu, Yuchen Liu, Qin Jin, Xinchao Wang, Haizhou Li
2022 arXiv   pre-print
In this work, we propose a Multi-modal Multi-scene Multi-label Emotional Dialogue dataset, M3ED, which contains 990 dyadic emotional dialogues from 56 different TV series, a total of 9,082 turns and 24,449  ...  To the best of our knowledge, M3ED is the first multimodal emotional dialogue dataset in Chinese. It is valuable for cross-culture emotion analysis and recognition.  ...  In the future, we will explore effective solutions to deal with the emotion imbalance challenge and learn multi-label emotion classification.  ... 
arXiv:2205.10237v1 fatcat:4pyirus22bhgnoc2fd4khyleom

MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations [article]

Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, Rada Mihalcea
2019 arXiv   pre-print
Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications.  ...  We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.  ...  Emotion shift of a speaker in a dialogue makes emotion recognition task very challenging.  ... 
arXiv:1810.02508v6 fatcat:xzih4k3ndrbkhpwcwuzonfwcyq

Conversational Transfer Learning for Emotion Recognition [article]

Devamanyu Hazarika, Soujanya Poria, Roger Zimmermann, Rada Mihalcea
2020 arXiv   pre-print
We propose an approach, TL-ERC, where we pre-train a hierarchical dialogue model on multi-turn conversations (source) and then transfer its parameters to a conversational emotion classifier (target).  ...  Recognizing emotions in conversations is a challenging task due to the presence of contextual dependencies governed by self- and inter-personal influences.  ...  We also gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan Xp GPU used for this research.  ... 
arXiv:1910.04980v3 fatcat:wz2a4txienhlba5r2q76cswtjq

A Review on Text-Based Emotion Detection – Techniques, Applications, Datasets, and Future Directions [article]

Sheetal Kusal, Shruti Patil, Jyoti Choudrie, Ketan Kotecha, Deepali Vora, Ilias Pappas
2022 arXiv   pre-print
The field of text-based emotion detection (TBED) is advancing to provide automated solutions to various applications, such as businesses, and finances, to name a few.  ...  Hence, it is essential for machines to understand emotions in opinions, feedback, and textual dialogues to provide emotionally aware responses to users in today's online world.  ...  The authors showed a lack of training data in the Arabic web blogs co-learning approach performed well with other baseline methods. 2) Multi-task learning -Multi-task learning is a method in which the  ... 
arXiv:2205.03235v1 fatcat:b3m25fg6xfc3leeym22eqysq5a

x-vectors meet emotions: A study on dependencies between emotion and speaker recognition [article]

Raghavendra Pappagari, Tianzi Wang, Jesus Villalba, Nanxin Chen, Najim Dehak
2020 arXiv   pre-print
We first show that knowledge learned for speaker recognition can be reused for emotion recognition through transfer learning. Then, we show the effect of emotion on speaker recognition.  ...  In this work, we explore the dependencies between speaker recognition and emotion recognition.  ...  SER is useful in applications such as detecting hate speech in social media, detecting patient's emotions, call routing based on emotion, actors analysis in the entertainment industry, mental health analysis  ... 
arXiv:2002.05039v1 fatcat:of6pzc52ovcyncanago2k6dwjq

EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion Recognition [article]

Maurice Gerczuk and Shahin Amiriparian and Sandra Ottl and Björn Schuller
2021 arXiv   pre-print
In this manuscript, the topic of multi-corpus Speech Emotion Recognition (SER) is approached from a deep transfer learning perspective.  ...  The corpus is then utilised to create a novel framework for multi-corpus speech emotion recognition, namely EmoNet.  ...  In a first set of experiments, denoted as "single-task transfer", four tasks out of EMOSET were chosen as base tasks for pre-training the deep learning architectures while for "multi-task transfer", all  ... 
arXiv:2103.08310v1 fatcat:qmrz2347jjhlngixy33lrak2gu

COSMIC: COmmonSense knowledge for eMotion Identification in Conversations [article]

Deepanway Ghosal, Navonil Majumder, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria
2020 arXiv   pre-print
In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge.  ...  Current state-of-the-art methods often encounter difficulties in context propagation, emotion shift detection, and differentiating between related emotion classes.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of A*STAR, the National Science Foundation, or the  ... 
arXiv:2010.02795v1 fatcat:ucfgtpjlpfer3mhthb6tphaq34

Comparing supervised and self-supervised embedding for ExVo Multi-Task learning track [article]

Tilak Purohit, Imen Ben Mahmoud, Bogdan Vlasenko, Mathew Magimai.-Doss
2022 arXiv   pre-print
The ICML Expressive Vocalizations (ExVo) Multi-task challenge 2022, focuses on understanding the emotional facets of the non-linguistic vocalizations (vocal bursts (VB)).  ...  For this challenge we study and compare two distinct embedding spaces namely, self-supervised learning (SSL) based embeddings and task-specific supervised learning based embeddings.  ...  Acknowledgements: This work was partially funded by the Swiss Science National Foundation through the Bridge Discovery project EMIL: Emotion in the loop -a step towards a comprehensive closed-loop deep  ... 
arXiv:2206.11968v1 fatcat:tvpqz2es7rcytayqgvgisihsua
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