Factors Affecting e-Commerce Satisfaction in Qatar: A Cross-Cultural Comparison

Abraham, Muth Mary; Choe, Pilsung

doi:10.1007/978-3-030-90238-4_34

Cited by 1 publication

(1 citation statement)

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“…The Thirty-Seventh AAAI Conference on Artificial Intelligence tistical properties from content and syntax (Shan et al 2019;Song et al 2020;Liu et al 2021a), speaker profiles (Yao et al 2020;Abraham and Choe 2021) to sentiment (Gadri et al 2021;Song et al 2021), act (Deng et al 2022) and deep semantics (Sun et al 2021;Kachuee et al 2021), which are regularly considered as key challenges in semantic understanding of dialogues. Different from previous studies that focus on modeling various properties, we find the sentiment states of the speakers provide rich clues for explicitly assisting with user satisfaction estimation.…”

Section: Service Staff Usermentioning

confidence: 99%

A Speaker Turn-Aware Multi-Task Adversarial Network for Joint User Satisfaction Estimation and Sentiment Analysis

Song

Kang

Liu

et al. 2023

AAAI

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User Satisfaction Estimation is an important task and increasingly being applied in goal-oriented dialogue systems to estimate whether the user is satisfied with the service. It is observed that whether the user’s needs are met often triggers various sentiments, which can be pertinent to the successful estimation of user satisfaction, and vice versa. Thus, User Satisfaction Estimation (USE) and Sentiment Analysis (SA) should be treated as a joint, collaborative effort, considering the strong connections between the sentiment states of speakers and the user satisfaction. Existing joint learning frameworks mainly unify the two highly pertinent tasks over cascade or shared-bottom implementations, however they fail to distinguish task-specific and common features, which will produce sub-optimal utterance representations for downstream tasks. In this paper, we propose a novel Speaker Turn-Aware Multi-Task Adversarial Network (STMAN) for dialogue-level USE and utterance-level SA. Specifically, we first introduce a multi-task adversarial strategy which trains a task discriminator to make utterance representation more task-specific, and then utilize a speaker-turn aware multi-task interaction strategy to extract the common features which are complementary to each task. Extensive experiments conducted on two real-world service dialogue datasets show that our model outperforms several state-of-the-art methods.

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