Sep 9–12, 2019

Schedule: Text, Language, and Speech sessions

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9:00am - 5:00pm Monday, September 9 & Tuesday, September 10
Location: 114
Delip Rao (AI Foundation)
Delip Rao explores natural language processing (NLP) with deep learning, walking you through neural network architectures and NLP tasks. You'll learn how to apply these architectures for those tasks. Read more.
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9:00am12:30pm Tuesday, September 10, 2019
Location: LL21 A/B
AI assistants are among the most in demand topics in tech. Get hands-on experience with Justina Petraityte as you develop intelligent AI assistants based entirely on machine learning and using only open source tools—Rasa NLU and Rasa Core. You'll learn the fundamentals of conversational AI and the best practices of developing AI assistants that scale and learn from real conversational data. Read more.
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9:00am12:30pm Tuesday, September 10, 2019
Location: LL21 E/F
Lukas Biewald (Weights & Biases)
Join Lukas Biewald to build and deploy long short-term memories (LSTMs), grated recurrent units (GRUs), and other text classification techniques using Keras and scikit-learn. Read more.
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1:30pm5:00pm Tuesday, September 10, 2019
Location: LL21 E/F
Joel Grus (Allen Institute for Artificial Intelligence)
AllenNLP is a PyTorch-based library designed to make it easy to do high-quality research in natural language processing (NLP). Joel Grus explains what modern neural NLP looks like, and you'll get your hands dirty training some models, writing some code, and learning how you can apply these techniques to your own datasets and problems. Read more.
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11:05am11:45am Wednesday, September 11, 2019
Location: Expo Hall 3
Huaixiu Zheng (Uber)
Uber applies natural language processing (NLP) and conversational AI in a number of business domains. Huaixiu Zheng details how Uber applies deep learning in the domain of NLP and conversational AI. You'll learn how Uber implements AI solutions in a real-world environment, as well as cutting-edge research in end-to-end dialogue systems. Read more.
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11:55am12:35pm Wednesday, September 11, 2019
Location: Expo Hall 3
Hagay Lupesko (Facebook)
Hagay Lupesko discusses AI-powered personalization at Facebook: the challenges and practical techniques applied to overcome these challenges. You will learn about deep learning based personalization modeling, scalable training, and the accompanying system design approaches that are applied in practice. Read more.
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11:55am12:35pm Wednesday, September 11, 2019
Location: LL21 E/F
Huaiyu Zhu (IBM Research - Almaden), Dulce Ponceleon (IBM Research - Almaden), Yunyao Li (IBM Research - Almaden)
Natural language understanding (NLU) underlies a wide range of applications and services. Rich resources available for English do not exist for most other languages, but the questions of how to expand these resources without duplicating effort and if it's possible to develop language-agnostic NLU-dependent applications remains. Huaiyu Zhu, Dulce Ponceleon, and Yunyao Li believe the answer is yes. Read more.
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11:55am12:35pm Wednesday, September 11, 2019
Location: 230 A
Joy Rimchala (Intuit), TJ Torres (Intuit), Xiao Xiao (Intuit), Hui Wang (Intuit)
Document understanding is a company-wide initiative at Intuit that aims to make data preparation and entry obsolete through the application of computer vision and machine learning. A team of data scientists, Joy Rimchala, TJ Torres, Xiao Xiao, and Hui Wang, detail the design and modeling methodologies used to build this platform as a service. Read more.
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1:45pm2:25pm Wednesday, September 11, 2019
Location: LL21 A/B
Yi Zhang (Rulai | University of California, Santa Cruz)
Consumers want everything now, at their fingertips, with very little effort. To meet these demands and compete, companies need to fundamentally rethink how they operate. Yi Zhang explores some predictions on how conversational technology will evolve from its current state in 2019. She outlines some common misunderstandings about the technologies and provides case studies from several industries. Read more.
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2:35pm3:15pm Wednesday, September 11, 2019
Location: Expo Hall 3
Joseph Spisak (Facebook), Hao Lu (Facebook)
Learn how PyTorch is being used to help accelerate the path from novel research to large-scale production deployment in computer vision, natural language processing, and machine translation at Facebook with Joseph Spisak and Hao Lu. Read more.
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4:00pm4:40pm Wednesday, September 11, 2019
Location: LL21 C/D
Ashish Bansal (Twitter)
Twitter has amazing and unique content generated at an enormous velocity internationally in multiple languages. Ashish Bansal provides you with insight into the unique recommendation system challenges at Twitter’s scale and what makes this a fun and challenging task. Read more.
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4:00pm4:40pm Wednesday, September 11, 2019
Location: Expo Hall 3
Moshe Wasserblat demonstrates the challenges and reviews the latest AI solutions in deploying natural language processing (NLP) in commercial environments, specifically dealing with the small amount of data available for training and scaling across different domains. Read more.
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4:00pm4:40pm Wednesday, September 11, 2019
Location: 230 B
Dexter Hadley (University of California, San Francisco)
Typically, large healthcare institutions have large-scale quantities of clinical data to facilitate precision medicine through an AI paradigm. However, this hardly translates into improved care. Dexter Hadley details how UCSF uses NLP to curate clinical data for over 1M mammograms and how deep learning, blockchain, and other approaches translates this into precision oncology. Read more.
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4:00pm4:40pm Wednesday, September 11, 2019
Location: 230 A
Stacy Ashworth (SelectData), Alberto Andreotti (John Snow Labs)
Much business data is still scanned or snapped documents, which is challenging. Stacy Ashworth and Alberto Andreotti explore a real-world case on reading, understanding, classifying, and acting on facts extracted from such image files using state-of-the-art, open source, deep learning-based optical character recognition (OCR), natural language processing (NLP), and forecasting libraries at scale. Read more.
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4:50pm5:30pm Wednesday, September 11, 2019
Location: LL21 E/F
Dylan Glas (Futurewei Technologies), Phoebe Liu (Figure Eight)
Robot technologies are becoming more capable and affordable. Yet even though technologies like natural language processing, mapping, and navigation are becoming more mature and standardized, it's often difficult to quantify human social behavior with algorithms. Dylan Glas and Phoebe Liu highlight some of the ongoing research to enable human-robot interaction. Read more.
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4:50pm5:30pm Wednesday, September 11, 2019
Location: 230 C
Sijun He (Twitter), Ali Mollahosseini (Twitter)
Twitter is what’s happening in the world right now. To connect users with the best content, Twitter needs to build up a deep understanding of its noisy and temporal text content. Sijun He provides an overview of the named entity recognition (NER) system at Twitter and explores the challenges Twitter faces to build and scale a large-scale deep learning system to annotate 500 million tweets per day. Read more.
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11:05am11:45am Thursday, September 12, 2019
Location: LL21 E/F
Chaitanya Shivade (IBM Research)
Using deep learning models to perform natural language inference (NLI) is a fundamental task in natural language processing. Chaitanya Shivade introduces a recently released dataset, MedNLI, for this task in the clinical domain, describes state-of-the-art models, explores how to adapt these into the healthcare domain, and details applications that can leverage these models. Read more.
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11:05am11:45am Thursday, September 12, 2019
Location: LL21 A/B
David Talby (Pacific AI)
New AI solutions in question answering, chatbots, structured data extraction, text generation, and inference all require deep understanding of the nuances of human language. David Talby outlines challenges, risks, and best practices for building NLU-based systems, drawing on examples and case studies from products and services built by Fortune 500 companies and startups over the past seven years. Read more.
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1:45pm2:25pm Thursday, September 12, 2019
Location: 230 A
Vijay Agneeswaran (Publicis Sapient), Abhishek Kumar (Publicis Sapient)
Vijay Agneeswaran and Abhishek Kumar explore multi-label text classification problems, where multiple tags or categories have to be associated with given text or documents. Multi-label text classification occurs in numerous real-world scenarios, for instance, in news categorization and in bioinformatics (such as the gene classification problem). Read more.
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1:45pm2:25pm Thursday, September 12, 2019
Location: LL21 A/B
Yael Gozin (Pfizer)
The size and the complexity of regulatory submissions to health authorities consistently increases, ut the process hasn't changed. The process of matching and verifying a data point in a table cell with its accurate source(s) is one of the main challenges of automating data quality checks. Yael Gozin details an an innovative, highly accurate, and efficient structured data verification method. Read more.
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1:45pm2:25pm Thursday, September 12, 2019
Location: Expo Hall 3
Stef Nelson-Lindall (Facebook)
PyText is a research to production platform that Facebook has leveraged to quickly develop state-of-the-art natural language processing (NLP) systems and deploy them to critical production use cases. Stef Nelson-Lindall explores several challenges with developing, training, and deploying real production systems with Torch, how to deal with them in NLP use cases, and more. Read more.
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2:35pm3:15pm Thursday, September 12, 2019
Location: 230 C
Anusua Trivedi (Microsoft)
Modern machine learning models often significantly benefit from transfer learning. Anusua Trivedi details a study of existing text transfer learning literature. She explores popular machine reading comprehension (MRC) algorithms and evaluates and compares the performance of the transfer learning approach for creating a question answering (QA) system for a book corpus using pretrained MRC models. Read more.
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4:00pm4:40pm Thursday, September 12, 2019
Location: 230 C
Jisheng Wang (Mist)
Increased complexity and business demands continue to make enterprise network operation more challenging. Jisheng Wang outlines the architecture of the first autonomous network operation solution along with two examples of ML-driven automated actions. He also shares some of his experiences and the lessons he learned applying ML/DL and AI to the development of SaaS-based enterprise solutions. Read more.
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4:50pm5:30pm Thursday, September 12, 2019
Location: LL21 A/B
Mayank Kejriwal (USC Information Sciences Institute)
Embeddings have emerged as an exciting by-product of the deep neural revolution and now apply universally to images, words, documents, and graphs. Many algorithms only require unlabeled datasets, which are plentiful in businesses. Mayank Kejriwal examines what these embeddings really are and how businesses can use them to bolster their AI strategy. Read more.
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4:50pm5:30pm Thursday, September 12, 2019
Location: 230 C
Ramsundar Janakiraman (Aruba Networks, A HPE Company)
While network protocols are the language of the conversations among devices in a network, these conversations are hardly ever labeled. Advances in capturing semantics present an opportunity for capturing access semantics to model user behavior. Ram Janakiraman explains how, with strong embeddings as a foundation, behavioral use cases can be mapped to NLP models of choice. Read more.

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