Personalized Interpolation: An Efficient Method to Tame Flexible Optimization Window Estimation

In the realm of online advertising, optimizing conversions is crucial for delivering relevant products to users and enhancing business outcomes. Predicting conversion events is challenging due to variable delays between user interactions, such as impressions or clicks, and the actual conversions. These delays differ significantly across various advertisers and products, necessitating distinct optimization time windows for targeted conversions. To address this, we introduce a novel approach named the \textit{Personalized Interpolation} method, which innovatively builds upon existing fixed conversion window models to estimate flexible conversion windows. This method allows for the accurate estimation of conversions across a variety of delay ranges, thus meeting the diverse needs of advertisers without increasing system complexity. To validate the efficacy of our proposed method, we conducted comprehensive experiments using ads conversion model. Our experiments demonstrate that this method not only achieves high prediction accuracy but also does so more efficiently than other existing solutions. This validation underscores the potential of our Personalized Interpolation method to significantly enhance conversion optimization in real-world online advertising systems, promising improved targeting and effectiveness in advertising strategies.

To Paraphrase or Not To Paraphrase: User-Controllable Selective Paraphrase Generation

In this article, we propose a paraphrase generation technique to keep the key phrases in source sentences during paraphrasing. We also develop a model called TAGPA with such technique, which has multiple pre-configured or trainable key phrase detector and a paraphrase generator. The paraphrase generator aims to keep the key phrases and increase the diversity of the paraphrased sentences. The key phrases can be entities provided by our user, like company names, people's names, domain-specific terminologies, etc., or can be learned from a given dataset.

Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents

Techniques for automatically extracting important content elements from business documents such as contracts, statements, and filings have the potential to make business operations more efficient. This problem can be formulated as a sequence labeling task, and we demonstrate the adaption of BERT to two types of business documents: regulatory filings and property lease agreements. There are aspects of this problem that make it easier than "standard" information extraction tasks and other aspects that make it more difficult, but on balance we find that modest amounts of annotated data (less than 100 documents) are sufficient to achieve reasonable accuracy. We integrate our models into an end-to-end cloud platform that provides both an easy-to-use annotation interface as well as an inference interface that allows users to upload documents and inspect model outputs.