Automated digitization of prescription images is a critical prerequisite to scale digital healthcare services such as online pharmacies. This is challenging in emerging markets since prescriptions are not digitized at source and patients lack the medical expertise to interpret prescriptions to place orders. In this paper, we present prescription digitization system for online medicine ordering built with minimal supervision. Our system uses a modular pipeline comprising a mix of ML and rule-based components for (a) image to text extraction, (b) segmentation into blocks and medication items, (c) medication attribute extraction, (d) matching against medicine catalog, and (e) shopping cart building. Our approach efficiently utilizes multiple signals like layout, medical ontologies, and semantic embeddings via LayoutLMv2 model to yield substantial improvement relative to strong baselines on medication attribute extraction. Our pipeline achieves +5.9% gain in precision@3 and +5.6% in recall@3 over catalog-based fuzzy matching baseline for shopping cart building for printed prescriptions.

For any E-commerce website it is a nontrivial problem to build enduring advertisements that attract shoppers. It is hard to pass the creative quality bar of the website, especially at a large scale. We thus propose a programmatic solution to generate product advertising headlines using retail content. We propose a state of the art application of Reinforcement Learning (RL) Policy gradient methods on Transformer (Vaswani et al., 2017) based Masked Language Models (Devlin et al., 2019). Our method creates the advertising headline by jointly conditioning on multiple products that a seller wishes to advertise. We demonstrate that our method outperforms existing Transformer and LSTM + RL methods in overlap metrics and quality audits. We also show that our model generated headlines outperform human submitted headlines in terms of both grammar and creative quality as determined by audits.

Advertising on e-commerce and social media sites deliver ad impressions at web scale on a daily basis driving value to both shoppers and advertisers. This scale necessitates programmatic ways of detecting unsuitable content in ads to safeguard customer experience and trust. This paper focusses on techniques for training text classification models under resource constraints, built as part of automated solutions for advertising content moderation. We show how weak supervision, curriculum learning and multi-lingual training can be applied effectively to fine-tune BERT and its variants for text classification tasks in conjunction with different data augmentation strategies. Our extensive experiments on multiple languages show that these techniques detect adversarial ad categories with a substantial gain in precision at high recall threshold over the baseline.