Challenge: Existing studies fail to provide comprehensive service satisfaction analysis . Existing models fail to include satisfaction polarity classification and sentimental utterance identification .
Approach: They propose a model that predicts customer sentiments and aggregates them into service satisfaction polarity.
Outcome: The proposed model predicts customer sentiments and aggregates them into service satisfaction polarity and reasoning clues.

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Challenge: Existing research on customer service dialogue generation generates generic responses from sellers . however, such cost prohibits small businesses, and multiturn dialogue generation is becoming more popular.
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Challenge: Existing non-task oriented dialogue systems can yield a relevant and fluent response, but sometimes make logical mistakes because of weak reasoning capabilities.
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Challenge: Existing methods to estimate turn and dialogue level user satisfaction employ hand-crafted features and rely on complex annotation schemes, which reduce generalizability of the trained models.
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Challenge: Existing work on retrieval-based context modeling for multi-turn conversation ignores interactions among previous utterances.
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Challenge: Existing work on building a conversational system for open domain human-machine conversation is attracting more attention . early models concatenate all utterances or independently encode each dialogue turn, which may lead to an inadequate understanding of dialogue status.
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Challenge: Existing approaches to multi-turn response generation for open-domain dialogues have a complexity problem . auxiliary tasks that relate to context understanding can guide the learning of the generation model .
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Challenge: Existing models for customer satisfaction prediction (CSP) focus on analyzing subjective customer satisfaction in conversational service, but they are hard to represent the important dynamic satisfaction states throughout the customer journey.
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Challenge: Existing studies largely overlook fine-grained sentiment dynamics expressed by customers . current methods often exhibit misalignment between aspects and sentiments .
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Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysis (2020.emnlp-main)

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Challenge: Existing methods to detect sentiment toward aspect categories ignore the fact that the sentiment of an aspect category mentioned in a sentence is an aggregation of the sentiments of the words indicating the aspect category in the sentence, which leads to suboptimal performance.
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