Videos
See AI2's full collection of videos on our YouTube channel.Viewing 181-190 of 257 videos
Deep Semantic Role Labeling: What Works and What’s Next
May 9, 2017 | Luheng HeSemantic role labeling (SRL) systems aim to recover the predicate-argument structure of a sentence, to determine essentially “who did what to whom”, “when”, and “where”. We introduce a new deep learning model for SRL that significantly improves the state of the art, along with detailed analyses to reveal its…
Building Lexical Resources for NLP
May 8, 2017 | Derry WijayaOne of the ways we can formulate natural language understanding is by treating it as a task of mapping natural language text to its meaning representation: entities and relations anchored to the world. Since verbs express relations over their arguments and adjuncts, a lexical resource about verbs can facilitate…
Language as a Scaffold for Accelerating Grounded Intelligence
May 2, 2017 | Mark YatskarIn this talk, we examine the role of language in enabling grounded intelligence. We consider two applications where language can be used as a scaffold for (a) allowing for the quick acquisition of large scale common sense knowledge, and (b) enabling broad coverage recognition of events in images. We present some…
Using Deep Learning to Understand Creative Language
April 19, 2017 | Mohit IyyerCreative language—the sort found in novels, film, and comics—contains a wide range of linguistic phenomena, from phrasal and sentential syntactic complexity to high-level discourse structures such as narrative and character arcs. In this talk, I explore how we can use deep learning to understand, generate, and…
Adventures in Analyzing and Presenting Bioscience Text
April 18, 2017 | Marti HearstAI2 researchers are making groundbreaking advances in machine interpretation of scientific and educational text and images. In our current research, we are interested in improving educational technology, especially automated and semi-automated guidance systems. In past work, we have been successful in leveraging…
Learning Agents That Interact With Humans
February 20, 2017 | He He XiyThe future of virtual assistants, self-driving cars, and smart homes require intelligent agents that work intimately with users. Instead of passively following orders given by users, an interactive agent must actively collaborate with people through communication, coordination, and user-adaptation. In this talk…
The Intelligent Management of Machine Learning
February 16, 2017 | Christopher LinResearch in artificial intelligence and machine learning (ML) has exploded in the last decade, bringing humanity to the cusp of self-driving cars, digital personal assistants, and unbeatable game-playing robots. My research, which spans the areas of AI, ML, Crowdsourcing, and Natural Language Processing (NLP…
Representation Learning for Language Units of Different Granularity
February 13, 2017 | Wenpeng YinWenpeng's talk mainly covers his work developing state-of-the-art deep neural networks to learn representations for different granularity of language units including single words, phrases, sentences, documents and knowledge graphs (KG). Specifically, he tries to deal with these questions: (a) So many pre-trained…
Learning Language through Interaction
January 25, 2017 | Hal DaumeMachine learning-based natural language processing systems are amazingly effective, when plentiful labeled training data exists for the task/domain of interest. Unfortunately, for broad coverage (both in task and domain) language understanding, we're unlikely to ever have sufficient labeled data, and systems must…
Situated Intelligent Interactive Systems
January 18, 2017 | Zhou YuCommunication is an intricate dance, an ensemble of coordinated individual actions. Imagine a future where machines interact with us like humans, waking us up in the morning, navigating us to work, or discussing our daily schedules in a coordinated and natural manner. Current interactive systems being developed…