This talk focuses on unsupervised dependency parsing—parsing sentences of a language into dependency trees without accessing the training data of that language. Different from most prior work that uses unsupervised learning to estimate the parsing parameters, we estimate the parameters by supervised training on synthetic languages. Our parsing framework has three major components: Synthetic language generation gives a rich set of training languages by mix-and-match over the real languages; surface-form feature extraction maps an unparsed corpus of a language into a fixed-length vector as the syntactic signature of that language; and, finally, language-agnostic parsing incorporates the syntactic signature during parsing so that the decision on each word token is reliant upon the general syntax of the target language.
The fundamental question we are trying to answer is whether some useful information about the syntax of a language could be inferred from its surface-form evidence (unparsed corpus). This is the same question that has been implicitly asked by previous papers on unsupervised parsing, which only assumes an unparsed corpus to be available for the target language. We show that, indeed, useful features of the target language can be extracted automatically from an unparsed corpus, which consists only of gold part-of-speech (POS) sequences. Providing these features to our neural parser enables it to parse sequences like those in the corpus. Strikingly, our system has no supervision in the target language. Rather, it is a multilingual system that is trained end-to-end on a variety of other languages, so it learns a feature extractor that works well. We show experimentally across multiple languages: (1) Features computed from the unparsed corpus improve parsing accuracy. (2) Including thousands of synthetic languages in the training yields further improvement. (3) Despite being computed from unparsed corpora, our learned task-specific features beat previous works’ interpretable typological features that require parsed corpora or expert categorization of the language
Dingquan Wang is a Ph.D. student working with Jason Eisner since 2014. His research interest is natural language processing (NLP) for low-resource languages. He received M.S. in Computer Science from Columbia University advised by Michael Collins and Rebecca Passonneau, and B.Eng from ACM Honored Class in Computer Science from Shanghai Jiao Tong University.
Hackerman Hall B-17
An exploration of the research on “Grit”, interleaved with the story of writing Practical Object-Oriented Design in Ruby, and the tale of a horrendous bike ride. This talk will convince you that you can accomplish anything.
Sandi Metz, author of Practical Object-Oriented Design in Ruby and 99 Bottles of OOP, believes in simple code and straightforward explanations. She prefers working software, practical solutions and lengthy bicycle trips (not necessarily in that order) and writes, consults, and teaches about object-oriented design
Hackerman Hall B-17
A recent New York Times article boldly stated that the Golden Age of Design is upon us. Our society is certainly in the midst of a great shift in how we view the world. In the past century, we have moved from the Age of Craft to the Industrial Age; we are currently on the cusp of the Age of Information. In the 20th century, innovations including the personal computer, the internet, smart phones, cloud computing, wearable computers and 3D and CNC printing have helped to radically change our conception of what we design. Today, designers no longer create products; they instead create platforms for open innovation.
This talk will reflect my walk through the discipline of design’s many eras and shifts, in order to understand this movement from designing products to designing platforms. The eras of user-centered design, experience design, service design, and systems design will be explored to better understand this migration. An alternative framing, product-service ecologies, will be introduced to stress a systemic and ecological view as a design approach to designing the products, services, environments, and platforms of today. A systemic view ensures that the designer can identify a need and understand the implications of designing something to impact the ecology in a positive way. A systemic view helps move the designer from problem solving to problem seeking, from modeling to understanding relationships, and from prototyping to perturbing the system to understand outcomes. It also ensures that designers are creating pragmatic and purposeful systems that will improve the state of today’s world.
Jodi Forlizzi is the Geschke Director and a Professor of Human-Computer Interaction in the School of Computer Science at Carnegie Mellon University. She is responsible for establishing design research as a legitimate form of research in HCI that is different from, but equally as important as, scientific and human science research. For the past 20 years, Jodi has advocated for design research in all forms, mentoring peers, colleagues, and students in its structure and execution, and today it is an important part of the CHI community.
Jodi’s current research interests include designing educational games that are engaging and effective, designing robots, AVs, and other technology services that use AI and ML to adapt to people’s needs, and designing for healthcare. Jodi is a member of the ACM CHI Academy and has been honored by the Walter Reed Army Medical Center for excellence in HRI design research. Jodi has consulted with Disney and General Motors to create innovative product-service systems.