AntProphet: an Intention Mining System behind Alipay's Intelligent Customer Service Bot

Chen, Cen; Zhang, Xiaolu; Ju, Sheng; Fu, Chilin; Tang, Caizhi; Zhou, Jun; Li, Xiaolong

doi:10.24963/ijcai.2019/935

Cited by 10 publications

(1 citation statement)

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“…Using deep learning to learn from data is an attractive alternative approach, not impeded by the limitations of handcrafted models. Deep learning is used, for instance, to classify intents of utterances in natural language processing (Chen et al 2019;Wen et al 2017) or activities in video analysis (Liu et al 2018). The sequentiality is a common feature of these two types of applications, as natural language consists of sequences of words, whereas activities in video analysis are short sequences of low-level actions.…”

Section: Introductionmentioning

confidence: 99%

A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features

Duhamel¹,

Maynard²,

Kabanza³

2019

Preprint

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The ability to infer the intentions of others, predict their goals, and deduce their plans are critical features for intelligent agents. For a long time, several approaches investigated the use of symbolic representations and inferences with limited success, principally because it is difficult to capture the cognitive knowledge behind human decisions explicitly. The trend, nowadays, is increasingly focusing on learning to infer intentions directly from data, using deep learning in particular. We are now observing interesting applications of intent classification in natural language processing, visual activity recognition, and emerging approaches in other domains. This paper discusses a novel approach combining few-shot and transfer learning with cross-domain features, to learn to infer the intent of an agent navigating in physical environments, executing arbitrary long sequences of actions to achieve their goals. Experiments in synthetic environments demonstrate improved performance in terms of learning from few samples and generalizing to unseen configurations, compared to a deep-learning baseline approach.

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Section: Introductionmentioning

confidence: 99%