Conferences, Lectures, & Seminars
Events for September
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NL Seminar-1. IMPROVING LOW RESOURCE NEURAL MACHINE TRANSLATION 2. LANGUAGE-INDEPENDENT TRANSLATION OF OUT OF VOCABULARY WORDS
Fri, Sep 08, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Nelson Liu and Leon Cheung, USC/ISI
Talk Title: 1. IMPROVING LOW RESOURCE NEURAL MACHINE TRANSLATION 2. LANGUAGE-INDEPENDENT TRANSLATION OF OUT OF VOCABULARY WORDS
Series: Natural Language Seminar
Abstract: 1. Statistical models have outperformed neural models in machine translation, until recently, with the introduction of the sequence to sequence neural model. However, this model's performance suffers greatly when starved of bilingual parallel data. This talk will discuss several strategies that try to overcome this low resource challenge, including modifications to the sequence to sequence model, transfer learning, data augmentation, and the use of monolingual data.
2. Neural machine translation is effective for language pairs with large datasets, but falls short to traditional methods e.g. phrase or syntax-based machine translation in the low resource setting. However, these classic approaches struggle to translate out of vocabulary tokens, a limitation that is amplified when there is little training data. In this work, we augment a syntax-based machine translation system with a module that provides translations of out of vocabulary tokens. We present several language-independent strategies for translation of unknown tokens, and benchmark their accuracy on an intrinsic out of vocabulary translation task across a typologically diverse dataset of sixteen languages. Lastly, we explore the effects of using the module to add rules to a syntax-based machine translation system on overall translation quality.
Biography: Leon Cheung is a second year undergraduate from UC San Diego. This summer he has been working with Jon May and Kevin Knight to improve neural machine translation for low resource languages.
Nelson Liu is an undergraduate at the University of Washington, where he works with Professor Noah Smith. His research interests lie at the intersection of machine learning and natural language processing. Previously, he worked at the Allen Institute for Artificial Intelligence on machine comprehension. He is currently a summer intern at ISI working with Professors Kevin Knight and Jonathan May.
Host: Marjan Ghazvininejad and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
The biomedical literature captures the most current biomedical knowledge and is a tremendously rich resource for research with over 26 million publications currently indexed in the US National Library of Medicineā€™s PubMed repository. Large-scale processin
Thu, Sep 28, 2017 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Karin Verspoor, University of Melbourne
Talk Title: The scientific literature as a resource for biological prediction and data validation
Abstract: The biomedical literature captures the most current biomedical knowledge and is a tremendously rich resource for research with over 26 million publications currently indexed in the US National Library of Medicines PubMed repository. Large scale processing of the literature enables direct biomedical knowledge discovery. In this presentation, I will introduce the use of text mining techniques for applications in protein function and phenotype prediction. I will also explore a novel alternative use of the literature to support curation of biological database records by cross checking their content with associated literature this work further broadens the value of the literature in bioinformatics applications.
Biography: Karin is a Professor in the School of Computing and Information Systems and Deputy Director of the Health and Biomedical Informatics Centre at the University of Melbourne. Her research focuses on text analytics and machine learning for biomedical applications, to enable knowledge extraction from unstructured data as well as to provide clinical decision support. A current active project is related to enabling precision medicine with machine learning.
Karin was previously the Scientific Director for Health and Life Sciences at NICTA. Prior to arriving in Australia from the United States she held research roles at the University of Colorado School of Medicine and Los Alamos National Laboratory, and spent 5 years developing language technology software in two start up companies.
Host: Gully Burns
Location: Information Science Institute (ISI) - 11th floor large conference room
Audiences: Everyone Is Invited
Contact: Kary LAU
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
AI Seminar
Fri, Sep 29, 2017 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Stefano Ermon, Stanford University
Talk Title: Learning with limited supervision
Abstract: Many of the recent successes of machine learning have been characterized by the availability of large quantities of labeled data. Nonetheless, we observe that humans are often able to learn with very few labeled examples or with only high level instructions for how a task should be performed. In this talk, I will present some new approaches for learning useful models in contexts where labeled training data is scarce or not available at all. I will first discuss and formally prove some limitations of existing training criteria used for learning hierarchical generative models. I will then introduce novel architectures and methods to overcome these limitations, allowing us to learn a hierarchy of interpretable features from unlabeled data. Finally, I will discuss ways to use prior knowledge (such as physics laws or simulators) to provide weak forms of supervision, showing how we can learn to solve useful tasks, including object tracking, without any labeled data.
Biography: Stefano Ermon is currently an Assistant Professor in the Department of Computer Science at Stanford University, where he is affiliated with the Artificial Intelligence Laboratory. He completed his PhD in computer science at Cornell in 2015. His research interests include techniques for scalable and accurate inference in graphical models, large-scale combinatorial optimization, and robust decision making under uncertainty, and is motivated by a range of applications, in particular ones in the emerging field of computational sustainability. Stefano's research has won several awards, including three Best Paper Awards, a World Bank Big Data Innovation Challenge, and was selected by Scientific American as one of the 10 World Changing Ideas in 2016. He is a recipient of the Sony Faculty Innovation Award and NSF CAREER Award.
Host: Aram Galstyan
Location: Information Science Institute (ISI) - 11th floor large conference room
Audiences: Everyone Is Invited
Contact: Kary LAU
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
