Processing Human Language with Recurrent Neural Networks and Multiple Training Signals

Yoav Goldberg - CS-Lecture
Thursday, 25.1.2018, 10:30
Room 601 Taub Bld.
Computer Science Department, Bar-Ilan University

While deep learning methods in Natural Language Processing are arguably overhyped, recurrent neural networks (RNNs), and in particular gated recurrent networks like the LSTM, emerge as very capable learners for sequential data. Thus, my group started using them everywhere. After briefly explaining what they are and why they are and giving a birds-eye overview of our work, I will describe a line of work in which we use LSTM encoders in a multi-task learning scenario. In these cases, we improve accuracy on a given task by supplementing the learning process with supervision signal from a related auxiliary task, using the LSTM as a shared representation. Short Bio: ========== Yoav Goldberg has been working in natural language processing for over a decade. He is a Senior Lecturer at the Computer Science Department at Bar-Ilan University, Israel. Prior to that he was a researcher at Google Research, New York. He received his PhD in Computer Science and Natural Language Processing from Ben Gurion University. He regularly reviews for NLP and Machine Learning venues, and serves at the editorial board of Computational Linguistics. He published over 50 research papers and received best paper and outstanding paper awards at major natural language processing conferences. His research interests include machine learning for natural language, structured prediction, syntactic parsing, processing of morphologically rich languages, and, in the past two years, neural network models with a focus on recurrent neural networks

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