One of the important modules in the chatbot is automated intent identification, i.e., understanding the user’s intention from the query text. A chatbot should be able to understand and answer a variety of such queries to help users with relevant information. The user queries can be pre-purchase questions, such as product specifications and delivery time related, or post-purchase queries, such as exchange and return. To provide a convenient shopping experience and to answer user queries at scale, conversational platforms are essential for e-commerce. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track) Label efficient semi-supervised conversational intent classification After positive results with A/B testing, the model is currently deployed in production. Experimental evaluation indicates that the proposed approach improves translation results by preserving/correcting English/Hinglish brand names. To reduce the latency of the model for the production deployment, we use knowledge distillation and quantization. The proposed model is generic and can be used with English as well as code-mix Hinglish queries alleviating the need for language detection. We warm-start the training from a public pre-trained checkpoint (such as BART/T5) and further adapt it for query translation using the domain data. Brand loss is a cross entropy loss computed using a denoising auto-encoder objective with brand name data. Further, we introduce a Brand loss in training the translation model. Brand names are added as tokens to the model vocabulary, and the token embeddings are randomly initialized. To achieve this, we use an additional dataset of popular Grocery brand names. In this paper, we propose a simple yet effective modification to the transformer training to preserve/correct Grocery brand names in the output while selectively translating the context words. In such queries, only the context (non-brand name) Hinglish words needs to be translated. However, the problem becomes non-trivial when the brand names themselves have Hinglish names and possibly have a literal English translation. An intuitive approach to dealing with code-mix queries is to train an encoder-decoder model to translate the query to English to perform the search. For periodic buying within a domain such as Grocery, consumers are generally inclined to buy certain brands of products.Due to a large non-English speaking population in India, we observe a significant percentage of code-mix Hinglish search queries e.g., sasta atta. Due to the democratization of e-commerce, many product companies are listing their goods for online shopping.
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