A survey on empathetic dialogue systems

Ma, Yukun; Nguyen, Khanh Linh; Xing, Frank Z.; Wang, Zhaoxia

doi:10.1016/j.inffus.2020.06.011

Cited by 167 publications

(65 citation statements)

References 45 publications

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“…Experimental results of word similarity, word analogy reasoning, text classification, sentiment analysis, and case studies validate the effectiveness of our method. In the future, we plan to explore comprehensive strategies for modeling phonological information by integrating our PCWE with other resources [7] or methods [6], and exploit the core idea of the proposed method to address other state-of-the-art NLP tasks, including sentiment analysis [57], reader emotion classification [58], review interpretation [59], empathetic dialogue systems [60], end-to-end dialogue systems [61], and stock market prediction [62,63].…”

Section: Discussionmentioning

confidence: 99%

Pronunciation-Enhanced Chinese Word Embedding

Yang

Xie²,

Cheng

et al. 2021

Cogn Comput

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Chinese word embeddings have recently garnered considerable attention. Chinese characters and their sub-character components, which contain rich semantic information, are incorporated to learn Chinese word embeddings. Chinese characters can represent a combination of meaning, structure, and pronunciation. However, existing embedding learning methods focus on the structure and meaning of Chinese characters. In this study, we aim to develop an embedding learning method that can make complete use of the information represented by Chinese characters, including phonology, morphology, and semantics. Specifically, we propose a pronunciation-enhanced Chinese word embedding learning method, where the pronunciations of context characters and target characters are simultaneously encoded into the embeddings. Evaluation of word similarity, word analogy reasoning, text classification, and sentiment analysis validate the effectiveness of our proposed method.

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Section: Discussionmentioning

confidence: 99%

Pronunciation-Enhanced Chinese Word Embedding

Yang

Xie²,

Cheng

et al. 2021

Cogn Comput

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“…SenticNet is a popular and common sense knowledge base for concept-level sentiment analysis [52]. Apart from common sense knowledge, it also considers affective knowledge via biologically-inspired and psychologicallymotivated emotional categorization model, wherein emotions are analyzed into independent, but connected via affective dimensions [54,56,59]. In this section, we present the effects of twoSenticNet-based sentic computing resources based on vector space model (word embedding)-Amazon word embedding (Amazon WE) [53] and AffectiveSpace [50] on the proposed CAT-BiGRU model.…”

Section: Effect Of Sentic Computing Resourcesmentioning

confidence: 99%

CAT-BiGRU: Convolution and Attention with Bi-Directional Gated Recurrent Unit for Self-Deprecating Sarcasm Detection

Kamal

Abulaish

2021

Cogn Comput

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Sarcasm detection has been a well-studied problem for the computational linguistic researchers. However, research related to different categories of sarcasm has still not gained much attention. Self-Deprecating Sarcasm (SDS) is a special category of sarcasm in which users apply sarcasm over themselves, and it is extensively used in social media platforms, mainly as an advertising tool for the brand endorsement, product campaign, and digital marketing with an aim to increase the sales volume. In this paper, we present a deep learning approach for detecting SDS on Twitter. We propose a novel Convolution and Attention with Bi-directional Gated Recurrent Unit (CAT-BiGRU) model, which consists of an input, embedding, convolutional, Bi-directional Gated Recurrent Unit (BiGRU), and two attention layers. The convolutional layer extracts SDS-based syntactic and semantic features from the embedding layer, BiGRU layer retrieves contextual information from the extracted features in both preceding and succeeding directions, and attention layers are used to retrieve SDS-based comprehensive context representation from the input texts. Finally, sigmoid function is employed to classify the input texts as a self-deprecating or non-self-deprecating sarcasm. Experiments are conducted over seven Twitter datasets to evaluate the proposed (CAT-BiGRU) model using standard evaluation metrics. The experimental results are impressive and significantly better than many neural network-based baselines and state-of-the-art methods. In this paper, we have highlighted biologically inspired and psychologically motivated basis of the proposed approach to examine its affective capabilities with respect to SenticNet. The efficacy of the proposed model is evaluated on two SenticNet-based sentic computing resources-Amazon word embedding and AffectiveSpace. Based on the experimental results, we conclude that deep learning-based approaches have potential to detect SDS in social media texts accurately.

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“…Emotional awareness and response are essential to achieve a more human interface, as humans are emotional beings. In a survey [30], a unified view of the emotional response of chatbots is provided. It concludes that emotional awareness is usually considered to play an essential role in user satisfaction.…”

Section: Related Workmentioning

confidence: 99%

JAICOB: A Data Science Chatbot

et al. 2020

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The application of natural language to improve students' interaction with information systems is demonstrated to be beneficial. In particular, advances in cognitive computing enable a new way of interaction that accelerates insight from existing information sources, thereby contributing to the process of learning. This work aims at researching the application of cognitive computing in blended learning environments. We propose a modular cognitive agent architecture for pedagogical question answering, featuring social dialogue (small talk), improved for a specific knowledge domain. This system has been implemented as a personal agent to assist students in learning Data Science and Machine Learning techniques. Its implementation includes the training of machine learning models and natural language understanding algorithms in a human-like interface. The effectiveness of the system has been validated through an experiment. INDEX TERMS Cognitive informatics, Educational technology, Human-computer interaction, Machine learning, Natural language processing.

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A survey on empathetic dialogue systems

Cited by 167 publications

References 45 publications

Pronunciation-Enhanced Chinese Word Embedding

Pronunciation-Enhanced Chinese Word Embedding

CAT-BiGRU: Convolution and Attention with Bi-Directional Gated Recurrent Unit for Self-Deprecating Sarcasm Detection

JAICOB: A Data Science Chatbot

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