James B. Wendt
James is a Software Engineer at Google DeepMind currently focusing on improving the structural data understanding capabilities of large generative models. Previously in Google Research, he focused on topics related to low-resource information extraction, semi-supervised learning, and data management and quality. Prior work also included developing large scale privacy-safe information extraction and data management systems for private corpora. Prior to joining Google, James earned his Ph.D. at UCLA under the guidance of Miodrag Potkonjak, where he explored methods for hardware security and low power circuit and system design. You can see his full list of publications on Google Scholar and
his personal site.
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FieldSwap: Data Augmentation for Effective Form-Like Document Extraction
Seth Ebner
IEEE 40th International Conference on Data Engineering (ICDE) (2024), pp. 4722-4732
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Extracting structured data from visually rich documents like invoices, receipts, financial statements, and tax forms is key to automating many business workflows. However, building extraction models in this domain often demands a large collection of high-quality training examples. To address this challenge, we introduce FieldSwap, a novel data augmentation technique specifically designed for such extraction problems. FieldSwap generates synthetic training examples by replacing key phrases indicative of one field with those corresponding to another. Our experiments on five diverse datasets demonstrate that incorporating FieldSwap-augmented data into the training process can enhance model performance by 1-11 F1 points, particularly when dealing with limited training data (10--100 documents). Additionally, we propose algorithms for automatically inferring key phrases from the training data. Our findings indicate that FieldSwap is effective regardless of whether key phrases are manually provided by human experts or inferred automatically.
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Selective Labeling: How to Radically Lower Data-Labeling Costs for Document Extraction Models
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, ACL, pp. 3847-3860
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Building automatic extraction models for visually rich documents like invoices, receipts, bills, tax forms, etc. has received significant attention lately. A key bottleneck in developing extraction models for new document types is the cost of acquiring the several thousand high-quality labeled documents that are needed to train a model with acceptable accuracy. In this paper, we propose selective labeling as a solution to this problem. The key insight is to simplify the labeling task to provide “yes/no” labels for candidate extractions predicted by a model trained on partially labeled documents. We combine this with a custom active learning strategy to find the predictions that the model is most uncertain about. We show through experiments on document types drawn from 3 different domains that selective labeling can reduce the cost of acquiring labeled data by 10× with a negligible loss in accuracy.
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Glean: Structured Extractions from Templatic Documents
Proceedings of the VLDB Endowment (2021), pp. 997-1005
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Extracting structured information from templatic documents is an important problem with the potential to automate many real-world business workflows such as payment, procurement, and payroll. The core challenge is that such documents can be laid out in virtually infinitely different ways. A good solution to this problem is one that generalizes well not only to known templates such as invoices from a known vendor, but also to unseen ones.
We developed a system called Glean to tackle this problem. Given a target schema for a document type and some labeled documents of that type, Glean uses machine learning to automatically extract structured information from other documents of that type. In this paper, we describe the overall architecture of Glean, and discuss three key data management challenges : 1) managing the quality of ground truth data, 2) generating training data for the machine learning model using labeled documents, and 3) building tools that help a developer rapidly build and improve a model for a given document type. Through empirical studies on a real-world dataset, we show that these data management techniques allow us to train a model that is over 5 F1 points better than the exact same model architecture without the techniques we describe. We argue that for such information-extraction problems, designing abstractions that carefully manage the training data is at least as important as choosing a good model architecture.
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Data-Efficient Information Extraction from Form-Like Documents
Document Intelligence Workshop @ KDD 2021
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Automating information extraction from form-like documents at scale is a pressing need due to its potential impact on automating business workflows across many industries like financial services, insurance, and healthcare. The key challenge is that form-like documents in these business workflows can be laid out in virtually infinitely many ways; hence, a good solution to this problem should generalize to documents with unseen layouts and languages. A solution to this problem requires a holistic understanding of both the textual segments and the visual cues within a document, which is non-trivial. While the natural language processing and computer vision communities are starting to tackle this problem, there has not been much focus on (1) data-efficiency, and (2) ability to generalize across different document types and languages.
In this paper, we show that when we have only a small number of labeled documents for training (~50), a straightforward transfer learning approach from a considerably structurally-different larger labeled corpus yields up to a 27 F1 point improvement over simply training on the small corpus in the target domain. We improve on this with a simple multi-domain transfer learning approach, that is currently in production use, and show that this yields up to a further 8 F1 point improvement. We make the case that data efficiency is critical to enable information extraction systems to scale to handle hundreds of different document-types, and learning good representations is critical to accomplishing this.
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Migrating a Privacy-Safe Information Extraction System to a Software 2.0 Design
Nguyen Ha Vo
Proceedings of the 10th Annual Conference on Innovative Data Systems Research (2020)
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This paper presents a case study of migrating a privacy-safe information extraction system for Gmail from a traditional rule-based architecture to a machine-learned Software 2.0 architecture. The key idea is to use the extractions from the existing rule-based system as training data to learn ML models
that in turn replace all the machinery for the rule-based system. The resulting system a) delivers better precision and recall, b) is significantly smaller in terms of lines of code, c) has been easier to maintain and improve, and d) has opened up the possibility of leveraging ML advances to build a cross-language extraction system even though our original training data was only in English. We describe challenges encountered during this migration around generation and management of training data, evaluation of models, and report on many traditional ``Software 1.0'' components we built to address them.
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Representation Learning for Information Extraction from Form-like Documents
Bodhisattwa Majumder
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), pp. 6495-6504
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We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images. We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates, and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains, but are also interpretable, as we show using loss cases.
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RiSER: Learning Better Representations for Richly Structured Emails
Furkan Kocayusufoğlu
Nguyen Ha Vo
Proceedings of the 2019 World Wide Web Conference, pp. 886-895
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Recent studies show that an overwhelming majority of emails are machine-generated and sent by businesses to consumers. Many large email services are interested in extracting structured data from such emails to enable intelligent assistants. This allows experiences like being able to answer questions such as ``What is the address of my hotel in New York?'' or ``When does my flight leave?''. A high-quality email classifier is a critical piece in such a system. In this paper, we argue that the rich formatting used in business-to-consumer emails contains valuable information that can be used to learn better representations. Most existing methods focus only on textual content and ignore the rich HTML structure of emails. We introduce RiSER (Richly Structured Email Representation) -- an approach for incorporating both the structure and content of emails. RiSER projects the email into a vector representation by jointly encoding the HTML structure and the words in the email. We then use this representation to train a classifier. To our knowledge, this is the first description of a neural technique for combining formatting information along with the content to learn improved representations for richly formatted emails. Experimenting with a large corpus of emails received by users of Gmail, we show that RiSER outperforms strong attention-based LSTM baselines. We expect that these benefits will extend to other corpora with richly formatted documents. We also demonstrate with examples where leveraging HTML structure leads to better predictions.
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Most consumer email in the world is machine-generated communication from a businesses to a human. Understanding the underlying templates that are used to instantiate these templates is a key step to enabling a variety of intelligent experiences. In this paper, we present the first description of the template-induction problem in an online setting for a planet-scale email system. While previous work has addressed the problem of discovering these templates using an offline batch job (perhaps architected as a MapReduce), discovering these templates online has several advantages. In this paper, we present the design of an online template induction system and describe the design choices we had to make. The resulting system handles online template induction over a stream of several billion emails a day. With the new system, new incoming email can be identified as belonging to a known template within minutes of discovering a template compared to several days worth of delay with the previous batch approach. Further, the online system has a resource consumption footprint that is 10x smaller than the batch approach. We also report on the surprising lesson we learned that conventional stream processing systems did not present a good framework on which to build this system. We hope that the lessons from this system help designers of future stream processing systems accommodate a broader range of applications like online template induction.
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Anatomy of a Privacy-Safe Large-Scale Information Extraction System Over Email
24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM (2018), pp. 734-743
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Extracting structured data from emails can enable several assistive experiences, such as reminding the user when a bill payment is due, answering queries about the departure time of a booked flight, or proactively surfacing an emailed discount coupon while the user is at that store.
This paper presents Juicer, a system for extracting information from email that is serving over a billion Gmail users daily. We describe how the design of the system was informed by three key principles: scaling to a planet-wide email service, isolating the complexity to provide a simple experience for the developer, and safeguarding the privacy of users (our team and the developers we support are not allowed to view any single email). We describe the design tradeoffs made in building this system, the challenges faced and the approaches used to tackle them. We present case studies of three extraction tasks implemented on this platform—bill reminders, commercial offers, and hotel reservations—to illustrate the effectiveness of the platform despite challenges unique to each task. Finally, we outline several areas of ongoing research in large-scale machine-learned information extraction from email.
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Learning Effective Embeddings for Machine Generated Emails with Applications to Email Category Prediction
Yu Sun
Luis Garcia Pueyo
Proceedings of the IEEE International Conference on Big Data (2018), pp. 1846-1855
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Machine-generated business-to-consumer (B2C) emails such as receipts, newsletters, and promotions constitute today a large portion of users' inbox. These emails reflect the users' interests and often are sequentially correlated, e.g., users interested in relocating may receive a sequence of messages on housing, moving, job availability, etc. We aim to infer (and eventually serve) the users' future interests by predicting the categories of their future emails. There are many good methods such as recurrent neural networks that can be applied for such predictions but in all cases the key to better performance is an effective representation of emails and users. To this end, we propose a general framework for embedding learning for emails and users, using as input only the sequence of B2C templates users receive and open. (A template is a B2C email stripped of all transient information related to specific users.) These learned embeddings allow us to identify both sequentially correlated emails and users with similar sequential interests. We can also use the learned embeddings either as input features or embedding initializers for email category predictions. Extensive experiments with millions of fully anonymized B2C emails demonstrate that the learned embeddings can significantly improve the prediction accuracy for future email categories. We hope that this effective yet simple embedding learning framework will inspire new machine intelligence applications that will improve the users' email experience.
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