Implicit Life Event Discovery From Call Transcripts Using Temporal Input Transformation Network

Nima Ebadi

doi:10.1109/ACCESS.2019.2954884

2019-01-01

Nima Ebadi

Brandon Lwowski

Mehrad Jaloli

DOI：10.1109/ACCESS.2019.2954884

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摘要

En 中文

Customer-agent conversations (i.e. call transcripts) are invaluable source for companies as they convey direct information from their customers implicit and explicit behaviour. Identifying customer-related events is an important task in customer services which is possible from the call transcripts. However, call centers produces a vast amount of transcripts which makes the manual or semi-manual processing of such raw datasets quite challenging. Furthermore, customer-agent call transcripts tend not to explicitly denote events that might be beneficial to customer services. Albeit being highly researched across multiple domains in the literature, event detection, especially implicit life event detection have not been well examined from call transcripts due to a lack of proper large-scale dataset. In this research, we propose a novel deep learning approach based on latent topic modeling and deep recurrent neural networks with memory units to automatically detect implicit events from a customer's history of call transcripts. These implicit events are detected prior to the report date of that event thereby not containing any explicit topic/feature. We provide a case study on a real-life, large-scale data of more than 800K call transcripts from a large financial services company in the U.S. to examine the practical features and challenges of this problem. The evaluation results demonstrate the potential applicability of our implicit life event detection as it achieves a macro-recall score of 53 (macro-f1 of 47.5) on a highly imbalanced test set, negative samples are 95% of the data. Our model beats the the state-of-the-art text classification benchmarks by macro-f1 score of 5.6 and macro-recall of 8.8 on average, and performs better than the ensemble of all single-document and sequential classification benchmarks albeit being significantly less complex. The comparison results show the importance as well as our model's capability of capturing the mutual information of a sequence of call transcripts in detecting the implicit life events.

Keyword:

Implicit event discovery

call transcripts

deep learning

recurrent neural network

machine learning

natural language processing

text classification

topic modeling

event detection