Priyanka Bhatt

Neeraj Agrawal, Saurabh Kumar, Priyanka Bhatt et al.

Hierarchical Text Classification (HTC) has recently gained traction given the ability to handle complex label hierarchy. This has found applications in domains like E- commerce, customer care and medicine industry among other real-world applications. Existing HTC models either encode label hierarchy separately and mix it with text encoding or guide the label hierarchy structure in the text encoder. Both approaches capture different characteristics of label hierarchy and are complementary to each other. In this paper, we propose a Hierarchical Text Classification using Contrastive Learning Informed Path guided hierarchy (HTC-CLIP), which learns hierarchy-aware text representation and text informed path guided hierarchy representation using contrastive learning. During the training of HTC-CLIP, we learn two different sets of class probabilities distributions and during inference, we use the pooled output of both probabilities for each class to get the best of both representations. Our results show that the two previous approaches can be effectively combined into one architecture to achieve improved performance. Tests on two public benchmark datasets showed an improvement of 0.99 - 2.37% in Macro F1 score using HTC-CLIP over the existing state-of-the-art models.

Saurabh Kumar, Sourav Bansal, Neeraj Agrawal et al.

Customer care is an essential pillar of the e-commerce shopping experience with companies spending millions of dollars each year, employing automation and human agents, across geographies (like US, Canada, Mexico, Chile), channels (like Chat, Interactive Voice Response (IVR)), and languages (like English, Spanish). SOTA pre-trained models like multilingual-BERT, fine-tuned on annotated data have shown good performance in downstream tasks relevant to Customer Care. However, model performance is largely subject to the availability of sufficient annotated domain-specific data. Cross-domain availability of data remains a bottleneck, thus building an intent classifier that generalizes across domains (defined by channel, geography, and language) with only a few annotations, is of great practical value. In this paper, we propose an embedder-cum-classifier model architecture which extends state-of-the-art domain-specific models to other domains with only a few labeled samples. We adopt a supervised fine-tuning approach with isotropic regularizers to train a domain-specific sentence embedder and a multilingual knowledge distillation strategy to generalize this embedder across multiple domains. The trained embedder, further augmented with a simple linear classifier can be deployed for new domains. Experiments on Canada and Mexico e-commerce Customer Care dataset with few-shot intent detection show an increase in accuracy by 20-23% against the existing state-of-the-art pre-trained models.

Subhadip Nandi, Neeraj Agrawal, Anshika Singh et al.

Customer service chatbots are conversational systems aimed at addressing customer queries, often by directing them to automated workflows. A crucial aspect of this process is the classification of the customer's intent. Presently, most intent classification models for customer care utilise only customer query for intent prediction. This may result in low-accuracy models, which cannot handle ambiguous queries. An ambiguous query like "I didn't receive my package" could indicate a delayed order, or an order that was delivered but the customer failed to receive it. Resolution of each of these scenarios requires the execution of very different sequence of steps. Utilizing additional information, such as the customer's order delivery status, in the right manner can help identify the intent for such ambiguous queries. In this paper, we have introduced a context-aware NLU model that incorporates both, the customer query and contextual information from the customer's order status for predicting customer intent. A novel selective attention module is used to extract relevant context features. We have also proposed a multi-task learning paradigm for the effective utilization of different label types available in our training data. Our suggested method, Multi-Task Learning Contextual NLU with Selective Attention Weighted Context (MTL-CNLU-SAWC), yields a 4.8% increase in top 2 accuracy score over the baseline model which only uses user queries, and a 3.5% improvement over existing state-of-the-art models that combine query and context. We have deployed our model to production for Walmart's customer care domain. Accurate intent prediction through MTL-CNLU-SAWC helps to better direct customers to automated workflows, thereby significantly reducing escalations to human agents, leading to almost a million dollars in yearly savings for the company.