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E-commerce query understanding is the process of inferring the shoppingintent of customers by extracting semantic meaning from their search queries.The recent progress of pre-trained masked language models (MLM) in naturallanguage processing is extremely attractive for developing effective queryunderstanding models. Specifically, MLM learns contextual text embedding viarecovering the masked tokens in the sentences. Such a pre-training processrelies on the sufficient contextual information. It is, however, less effectivefor search queries, which are usually short text. When applying masking toshort search queries, most contextual information is lost and the intent of thesearch queries may be changed. To mitigate the above issues for MLMpre-training on search queries, we propose a novel pre-training taskspecifically designed for short text, called Extended Token Classification(ETC). Instead of masking the input text, our approach extends the input byinserting tokens via a generator network, and trains a discriminator toidentify which tokens are inserted in the extended input. We conductexperiments in an E-commerce store to demonstrate the effectiveness of ETC.

Short Text Pre-training with Extended Token Classification for E-commerce Query Understanding