Forum for Artificial Intelligence

The Forum for Artificial Intelligence meets every other week (or so) to discuss scientific, philosophical, and cultural issues in artificial intelligence. Both technical research topics and broader inter-disciplinary aspects of AI are covered, and all are welcome to attend!

Processing Code-Switched Text

Thamar Solorio (University of Texas at Dallas)

Friday, May 2 @ 11:00am in ACES 2.402

Code-switching is an interesting linguistic phenomenon commonly observed in highly bilingual communities. It consists of mixing languages in the same conversational event. Despite its popularity, this type of discourse has received very little attention from the natural language processing community. Most of the work in this area attempts to solve problems where the language samples, either spoken or written, are monolingual. We recently started working on developing a part-of-speech tagger for Spanish-English code-switched text. In the first half of this talk I will discuss results of different approaches to solve the tagging problem by taking advantage of existing resources for both languages. The long-term goal of this research is to develop a full syntactic parser for English-Spanish code-switched text, commonly known as Spanglish, that can be exploited to tackle higher-level tasks on mixed-language sources. Although the work is focused on English-Spanish bilingual discourse, the knowledge acquired from this project can later be extended to other language combinations. In the second half, I will discuss a related project aimed at exploiting our bilingual tagger to develop an automated screening tool for the early identification of Specific Language Impairment in Spanish-English bilingual children.

Object Recognition by Scene Alignment

Antonio Torralba (Massachusetts Institute of Technology)

Friday, May 23 @ 11:00am in ACES 2.402

Object detection and recognition is generally posed as a matching problem between the object representation and the image features (e.g., aligning pictorial cues, shape correspondence, constellations of parts, etc.) while rejecting the background features using an outlier process. In this work, we take a different approach: we formulate the object detection problem as a problem of aligning elements of the entire scene. The background, instead of being treated as a set of outliers, is used to guide the detection process. Our approach relies on the observation that when we have a big enough database then we can find with high probability some images in the database very close to a query image, as in similar scenes with similar objects arranged in similar spatial configurations. If the images in the retrieval set are partially labeled, then we can transfer the knowledge of the labeling to the query image, and the problem of object recognition becomes a problem of aligning scene regions. But, can we find a dataset large enough to cover a large number of scene configurations? Given an input image, how do we find a good retrieval set, and, finally, how we do transfer the labels to the input image? We will use two datasets; 1) the LabelMe dataset, which contains more than 10,000 labeled images with over 180,000 annotated objects. 2) The tiny images dataset: A dataset of weakly labeled images with more than 79,000,000 images. We use this database to perform object and scene classification, examining performance over a range of semantic levels. Work in collaboration with Rob Fergus, Bryan Russell, Ce Liu and William T. Freeman Additional information and links to relevant papers can be found at: URL: http://people.csail.mit.edu/torralba/tinyimages/

Title TBA

Rich Sutton (University of Alberta)

Tuesday, June 24 @ 11:00am in ACES 2.402

Abstract TBA