Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training

Xu, Peng; Madotto, Andrea; Wu, Chien-Sheng; Park, Ji Ho; Fung, Pascale

doi:10.18653/v1/w18-6243

Cited by 59 publications

(41 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The tone of system responses needs to be adjusted according to user's emotional states and affects. So the system needs to ground in affect or emotion of the user [30,104,109].…”

Section: Open-domain Dialog Vs Task-oriented Dialogmentioning

confidence: 99%

Challenges in Building Intelligent Open-domain Dialog Systems

Huang

Zhu

Gao

2020

ACM Trans. Inf. Syst.

204

122

View full text Add to dashboard Cite

There is a resurgent interest in developing intelligent open-domain dialog systems due to the availability of large amounts of conversational data and the recent progress on neural approaches to conversational AI [17]. Unlike traditional task-oriented bots, an open-domain dialog system aims to establish long-term connections with users by satisfying the human need for communication, affection, and social belonging. This paper reviews the recent works on neural approaches that are devoted to addressing three challenges in developing such systems: semantics, consistency, and interactiveness. Semantics requires a dialog system to not only understand the content of the dialog but also identify user's social needs during the conversation. Consistency requires the system to demonstrate a consistent personality to win users trust and gain their long-term confidence. Interactiveness refers to the system's ability to generate interpersonal responses to achieve particular social goals such as entertainment, conforming, and task completion.The works we select to present here is based on our unique views and are by no means complete. Nevertheless, we hope that the discussion will inspire new research in developing more intelligent dialog systems.

show abstract

“…The tone of system responses needs to be adjusted according to user's emotional states and affects. So the system needs to ground in affect or emotion of the user [30,104,109].…”

Section: Open-domain Dialog Vs Task-oriented Dialogmentioning

confidence: 99%

Challenges in Building Intelligent Open-domain Dialog Systems

Huang

Zhu

Gao

2020

ACM Trans. Inf. Syst.

204

122

View full text Add to dashboard Cite

show abstract

“…We finally experiment with neural models, although our dataset is relatively small. We train both a two-layer bidirectional Gated Recurrent Neural Network (GRNN) (Cho et al, 2014) and Convolutional Neural Network (CNN) (as designed in Kim (2014)) with parallel filters of size 2 and 3, as these have been shown to be effective in the literature on emotion detection in text (e.g., Xu et al (2018); Abdul-Mageed and Ungar (2017)). Because neural models require large amounts of data, we do not cull the data by annotator agreement for these experiments and use all the labeled data we have.…”

Section: Supervised Modelsmentioning

confidence: 99%

Dreaddit: A Reddit Dataset for Stress Analysis in Social Media

Turcan

McKeown

2019

Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

View full text Add to dashboard Cite

Stress is a nigh-universal human experience, particularly in the online world. While stress can be a motivator, too much stress is associated with many negative health outcomes, making its identification useful across a range of domains. However, existing computational research typically only studies stress in domains such as speech, or in short genres such as Twitter. We present Dreaddit, a new text corpus of lengthy multi-domain social media data for the identification of stress. Our dataset consists of 190K posts from five different categories of Reddit communities; we additionally label 3.5K total segments taken from 3K posts using Amazon Mechanical Turk. We present preliminary supervised learning methods for identifying stress, both neural and traditional, and analyze the complexity and diversity of the data and characteristics of each category.

show abstract

“…Embeddings Previous works have extensively explored different representations such as word (Mikolov et al, 2013;Pennington et al, 2014;Grave et al, 2018;Xu et al, 2018), subword (Sennrich et al, 2016;Heinzerling and Strube, 2018), and character (dos Santos and Zadrozny, 2014; Wieting et al, 2016). Lample et al (2016) has successfully concatenated character and word embeddings to their model, showing the potential of combining multiple representations.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition

Winata

Lin

Shin

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

Self Cite

View full text Add to dashboard Cite

In countries that speak multiple main languages, mixing up different languages within a conversation is commonly called codeswitching. Previous works addressing this challenge mainly focused on word-level aspects such as word embeddings. However, in many cases, languages share common subwords, especially for closely related languages, but also for languages that are seemingly irrelevant. Therefore, we propose Hierarchical Meta-Embeddings (HME) that learn to combine multiple monolingual word-level and subword-level embeddings to create language-agnostic lexical representations. On the task of Named Entity Recognition for English-Spanish code-switching data, our model achieves the state-of-the-art performance in the multilingual settings. We also show that, in cross-lingual settings, our model not only leverages closely related languages, but also learns from languages with different roots. Finally, we show that combining different subunits are crucial for capturing codeswitching entities.

show abstract

Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training

Cited by 59 publications

References 35 publications

Challenges in Building Intelligent Open-domain Dialog Systems

Challenges in Building Intelligent Open-domain Dialog Systems

Dreaddit: A Reddit Dataset for Stress Analysis in Social Media

Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition

Contact Info

Product

Resources

About