Videos
See AI2's full collection of videos on our YouTube channel.Viewing 141-150 of 249 videos
Acquiring Lexical Semantic Knowledge
June 7, 2018 | Vered ShwartzRecognizing lexical inferences is one of the building blocks of natural language understanding. Lexical inference corresponds to a semantic relation that holds between two lexical items (words and multi-word expressions), when the meaning of one can be inferred from the other. In reading comprehension, for…Found in Translation: the journey to achieving human parity in Machine Translation
May 18, 2018 | Hany HassanMachine translation has made rapid advances in recent years. Millions of people are using it today in online translation systems and mobile applications in order to communicate across language barriers. The question naturally arises whether such systems can approach or achieve parity with human translations. In…Deep Representation Learning with Induced Structural Priors
May 8, 2018 | Saining XieWith the support of big-data and big-compute, deep learning has reshaped the landscape of research and applications in artificial intelligence. Whilst traditional hand-guided feature engineering in many cases is simplified, the deep network architectures become increasingly more complex. A central question is if…New Resources and Ideas for Semantic Parsing
April 20, 2018 | Kyle RichardsonIn this talk, I will give an overview of research being done at the University of Stuttgart on semantic parser induction and natural language understanding. The main topic, semantic parser induction, relates to the problem of learning to map input text to full meaning representations from parallel datasets. Such…Open Loop Hyperparameter Optimization and Determinantal Point Processes
April 10, 2018 | Jesse DodgeDriven by the need for parallelizable hyperparameter optimization methods, we study open loop search methods: sequences that are predetermined and can be generated before a single configuration is evaluated. Examples include grid search, uniform random search, low discrepancy sequences, and other sampling…Connecting Vision and Language for Interpretation, Grounding and Imagination
April 2, 2018 | Rama VedantamUnderstanding how to model vision and language jointly is a long-standing challenge in artificial intelligence. Vision is one of the primary sensors we use to perceive the world, while language is our data structure to represent and communicate knowledge. In this talk, we will take up three lines of attack to…Robust Text Correction for Grammar and Fluency
March 30, 2018 | Keisuke SakaguchiRobustness has always been a desirable property for natural language processing. In many cases, NLP models (e.g., parsing) and downstream applications (e.g., MT) perform poorly when the input contains noise such as spelling errors, grammatical errors, and disfluency. In this talk, I will present three recent…Debiasing natural language evaluation with humans in the loop
March 28, 2018 | Arun ChagantyA significant challenge in developing systems for tasks such as knowledge base population, text summarization or question answering is simply evaluating their performance: existing fully-automatic evaluation techniques rely on an incomplete set of “gold” annotations that can not adequately cover the range of…Text Representation, Retrieval, and Understanding with Knowledge Graphs
March 26, 2018 | Chenyan XiongSearch engines and other information systems have started to evolve from retrieving documents to providing more intelligent information access. However, the evolution is still in its infancy due to computers’ limited ability in representing and understanding human language. This talk will present my work…Internal Representations in Deep Learning for Language and Speech Processing
March 7, 2018 | Yonatan BelinkovLanguage technology has become pervasive in everyday life, powering applications like Apple’s Siri or Google’s Assistant. Neural networks are a key component in these systems thanks to their ability to model large amounts of data. Contrary to traditional systems, models based on deep neural networks (a.k.a. deep…