Zara The Supergirl: An Empathetic Personality Recognition System

Fung, Pascale; Dey, Anik; Siddique, Farhad Bin; Lin, Ruixi; Yang, Yang; Wan, Yan; Chan, Ho Yin

doi:10.18653/v1/n16-3018

Cited by 26 publications

(21 citation statements)

References 12 publications

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“…Personality is a significant factor of communication in human-human interaction [1,2], and as virtual agents and dialogue systems get more intelligent, we expect them to identify and adapt to different user personalities. Having such a personality recognition module in dialogue systems will enable us to have more intelligent and personal human-agent conversations in the future [3,4].…”

Section: Introductionmentioning

confidence: 99%

Bilingual Word Embeddings for Cross-Lingual Personality Recognition Using Convolutional Neural Nets

Siddique

Fung

2017

Interspeech 2017

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We propose a multilingual personality classifier that uses text data from social media and Youtube Vlog transcriptions, and maps them into Big Five personality traits using a Convolutional Neural Network (CNN). We first train unsupervised bilingual word embeddings from an English-Chinese parallel corpus, and use these trained word representations as input to our CNN. This enables our model to yield relatively high cross-lingual and multilingual performance on Chinese texts, after training on the English dataset for example. We also train monolingual Chinese embeddings from a large Chinese text corpus and then train our CNN model on a Chinese dataset consisting of conversational dialogue labeled with personality. We achieve an average F-score of 66.1 in our multilingual task compared to 63.3 F-score in cross-lingual, and 63.2 F-score in the monolingual performance.

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Section: Introductionmentioning

confidence: 99%

Bilingual Word Embeddings for Cross-Lingual Personality Recognition Using Convolutional Neural Nets

Siddique

Fung

2017

Interspeech 2017

Self Cite

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“…Fung et al () demonstrated a virtual interaction dialogue system that has incorporated sentiment, emotion, and personality recognition capabilities trained by deep learning models.…”

Section: Multimodal Data For Sentiment Analysismentioning

confidence: 99%

Deep learning for sentiment analysis: A survey

Zhang

Wang

Liu

2018

WIREs Data Min & Knowl

1,315

622

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Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state‐of‐the‐art prediction results. Along with the success of deep learning in many application domains, deep learning is also used in sentiment analysis in recent years. This paper gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis. This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts Algorithmic Development > Text Mining

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“…The research can be tracked back to the GRUNDY system (Rich, 1979) which categorizes users in terms of hand-crafted sets of user properties for book recommendation. Other systems have focused on different aspects of users, e.g., the expertise level of the user in a specific domain (Chin, 1986;Sleeman, 1985;Paris, 1987;Hovy, 1987), the user's intent and plan (Allen and Perrault, 1980;Carberry, 1983;Litman, 1986;Moore and Paris, 1992), and the user's personality (Mairesse and Walker, 2006;DeVault et al, 2014;Fung et al, 2016;. User modeling has also been employed for personalized topic suggestion in recent Alexa Prize socialbots, using a pre-defined mapping between personality types and topics , or a conditional random field sequence model with hand-crafted user and context features (Ahmadvand et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Speaker Model for Conversational Interactions

Cheng¹,

Fang²,

Ostendorf³

2019

Proceedings of the 2019 Conference of the North

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Individual differences in speakers are reflected in their language use as well as in their interests and opinions. Characterizing these differences can be useful in human-computer interaction, as well as analysis of human-human conversations. In this work, we introduce a neural model for learning a dynamically updated speaker embedding in a conversational context. Initial model training is unsupervised, using context-sensitive language generation as an objective, with the context being the conversation history. Further finetuning can leverage task-dependent supervised training. The learned neural representation of speakers is shown to be useful for content ranking in a socialbot and dialog act prediction in human-human conversations. 1

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Zara The Supergirl: An Empathetic Personality Recognition System

Cited by 26 publications

References 12 publications

Bilingual Word Embeddings for Cross-Lingual Personality Recognition Using Convolutional Neural Nets

Bilingual Word Embeddings for Cross-Lingual Personality Recognition Using Convolutional Neural Nets

Deep learning for sentiment analysis: A survey

A Dynamic Speaker Model for Conversational Interactions

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