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James B. Wendt

James B. Wendt

James B. Wendt is currently a software engineer at Google Research focusing on topics related to large-scale and privacy-safe information extraction, data management systems, and applied ML. Before that, he earned his Ph.D. in Computer Science at UCLA, where he explored methods for hardware security and low power design.
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      Preview abstract 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. View details
      Preview abstract 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. View details
      Preview abstract 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. View details
      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)
      Preview abstract 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. View details
      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
      Preview abstract 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. View details
      Preview abstract 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. View details
      RiSER: Learning Better Representations for Richly Structured Emails
      Furkan Kocayusufoğlu
      Nguyen Ha Vo
      Proceedings of the 2019 World Wide Web Conference, pp. 886-895
      Preview abstract 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. View details
      Preview abstract 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. View details
      Preview abstract A vast majority of the emails received by people today are machine-generated by businesses communicating with consumers. While some emails originate as a result of a transaction (e.g., hotel or restaurant reservation confirmations, online purchase receipts, shipping notifications, etc.), a large fraction are commercial emails promoting an offer (a special sale, free shipping, available for a limited time, etc.). The sheer number of these promotional emails makes it difficult for users to read all these emails and decide which ones are actually interesting and actionable. In this paper, we tackle the problem of extracting information from commercial emails promoting an offer to the user. This information enables an email platform to build several new experiences that can unlock the value in these emails without the user having to navigate and read all of them. For instance, we can highlight offers that are expiring soon, or display a notification when there's an unexpired offer from a merchant if your phone recognizes that you are at that merchant's store. A key challenge in extracting information from such commercial emails is that they are often image-rich and contain very little text. Training a machine learning (ML) model on a rendered image-rich email and applying it to each incoming email can be prohibitively expensive. In this paper, we describe a cost-effective approach for extracting signals from both the text and image content of commercial emails in the context of a free email platform that serves over a billion users around the world. The key insight is to leverage the template structure of emails, and use off-the-shelf OCR techniques to obtain the text from images to augment the existing text features offline. Compared to a text-only approach, we show that we are able to identify 9.12% more email templates corresponding to ~5% more emails being identified as offers. Interestingly, our analysis shows that this 5% improvement in coverage is across the board, irrespective of whether the emails were sent by large merchants or small local merchants, allowing us to deliver an improved experience for everyone. View details
      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
      Preview abstract 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. View details
      Email Category Prediction
      Aston Zhang
      Luis Garcia Pueyo
      Companion Proc. of the 26th International World Wide Web Conference (2017), pp. 495-503
      Preview abstract According to recent estimates, about 90% of consumer received emails are machine-generated. Such messages include shopping receipts, promotional campaigns, newsletters, booking confirmations, etc. Most such messages are created by populating a fixed template with a small amount of personalized information, such as name, salutation, reservation numbers, dates, etc. Web mail providers (Gmail, Hotmail, Yahoo) are leveraging the structured nature of such emails to extract salient information and use it to improve the user experience: e.g. by automatically entering reservation data into a user calendar, or by sending alerts about upcoming shipments. To facilitate these extraction tasks it is helpful to classify templates according to their category, e.g. restaurant reservations or bill reminders, since each category triggers a particular user experience. Recent research has focused on discovering the causal thread of templates, e.g. inferring that a shopping order is usually followed by a shipping confirmation, an airline booking is followed by a confirmation and then by a “ready to check in” message, etc. Gamzu et al. took this idea one step further by implementing a method to predict the template category of future emails for a given user based on previously received templates. The motivation is that predicting future emails has a wide range of potential applications, including better user experiences (e.g. warning users of items ordered but not shipped), targeted advertising (e.g. users that recently made a flight reservation may be interested in hotel reservations), and spam classification (a message that is part of a legitimate causal thread is unlikely to be spam). The gist of the Gamzu et al. approach is modeling the problem as a Markov chain, where the nodes are templates or temporal events (e.g. the first day of the month). This paper expands on their work by investigating the use of neural networks for predicting the category of emails that will arrive during a fixed-sized time window in the future. We consider two types of neural networks: multi-layer perceptrons (MLP), a type of feedforward neural network; and long short-term memory (LSTM), a type of recurrent neural network. For each type of neural network, we explore the effects of varying their configuration (e.g. number of layers or number of neurons) and hyper-parameters (e.g. drop-out ratio). We find that the prediction accuracy of neural networks vastly outperforms the Markov chain approach, and that LSTMs perform slightly better than MLPs. We offer some qualitative interpretation of our findings and identify some promising future directions. View details
      Template Induction over Unstructured Email Corpora
      Lluís Garcia-Pueyo
      Ivo Krka
      Tobias Kaufmann
      Proc. of the 26th International World Wide Web Conference (2017), pp. 1521-1530
      Preview abstract Unsupervised template induction over email data is a central component in applications such as information extraction, document classification, and auto-reply. The benefits of automatically generating such templates are known for structured data, e.g. machine generated HTML emails. However much less work has been done in performing the same task over unstructured email data. We propose a technique for inducing high quality templates from plain text emails at scale based on the suffix array data structure. We evaluate this method against an industry-standard approach for finding similar content based on shingling, running both algorithms over two corpora: a synthetically created email corpus for a high level of experimental control, as well as user-generated emails from the well-known Enron email corpus. Our experimental results show that the proposed method is more robust to variations in cluster quality than the baseline and templates contain more text from the emails, which would benefit extraction tasks by identifying transient parts of the emails. Our study indicates templates induced using suffix arrays contain approximately half as much noise (measured as entropy) as templates induced using shingling. Furthermore, the suffix array approach is substantially more scalable, proving to be an order of magnitude faster than shingling even for modestly-sized training clusters. Public corpus analysis shows that email clusters contain on average 4 segments of common phrases, where each of the segments contains on average 9 words, thus showing that templatization could help users reduce the email writing effort by an average of 35 words per email in an assistance or auto-reply related task. View details
      Hierarchical Label Propagation and Discovery for Machine Generated Email
      Lluis Garcia-Pueyo
      Vanja Josifovski
      Ivo Krka
      Amitabh Saikia
      Sujith Ravi
      Proceedings of the International Conference on Web Search and Data Mining (WSDM), ACM (2016), pp. 317-326
      Preview abstract Machine-generated documents such as email or dynamic web pages are single instantiations of a pre-defined structural template. As such, they can be viewed as a hierarchy of template and document specific content. This hierarchical template representation has several important advantages for document clustering and classification. First, templates capture common topics among the documents, while filtering out the potentially noisy variabilities such as personal information. Second, template representations scale far better than document representations since a single template captures numerous documents. Finally, since templates group together structurally similar documents, they can propagate properties between all the documents that match the template. In this paper, we use these advantages for document classification by formulating an efficient and effective hierarchical label propagation and discovery algorithm. The labels are propagated first over a template graph (constructed based on either term-based or topic-based similarities), and then to the matching documents. We evaluate the performance of the proposed algorithm using a large donated email corpus and show that the resulting template graph is significantly more compact than the corresponding document graph and the hierarchical label propagation is both efficient and effective in increasing the coverage of the baseline document classification algorithm. We demonstrate that the template label propagation achieves more than 91% precision and 93% recall, while increasing the label coverage by more than 11%. View details
      Preview abstract We present a new method for spatiotemporal assignment and scheduling of energy harvesters on a medical shoe tasked with measuring gait diagnostics. While prior work exists on the application of dielectric elastomers (DEs) for energy scavenging on shoes, current literature does not address the issues of placement and timing of these harvesters, nor does it address integration into existing sensing systems. We solve these issues and present a self-sustaining medical shoe that harvests energy from human ambulation while simultaneously measuring gait characteristics most relevant to medical diagnosis. View details
      Semantics-driven sensor configuration for energy reduction in medical sensor networks
      Saro Meguerdichian
      Miodrag Potkonjak
      Proceedings of the 2012 ACM/IEEE international symposium on Low power electronics and design, ACM, pp. 303-308
      Preview abstract Traditional optimization methods for large multisensory networks often use sensor array reduction and sampling techniques that attempt to reduce energy while retaining full predictability of the raw sensed data. For systems such as medical sensor networks, raw data prediction is unnecessary, rather, only relevant semantics derived from the raw data are essential. We present a new method for sensor fusion, array reduction, and subsampling that reduces both energy and cost through semantics-driven system configuration. Using our method, we reduce the energy requirements of a medical shoe by a factor of 17.9 over the original system configuration while maintaining semantic relevance. View details
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