Google Translate welcomes you to the Indic web

Posted by Ashish Venugopal, Research Scientist

(Cross-posted on the Translate Blog and the Official Google Blog)

 

Beginning today, you can explore the linguistic diversity of the Indian sub-continent with Google Translate, which now supports five new experimental alpha languages: Bengali, Gujarati, Kannada, Tamil and Telugu. In India and Bangladesh alone, more than 500 million people speak these five languages. Since 2009, we’ve launched a total of 11 alpha languages, bringing the current number of languages supported by Google Translate to 63.

Indic languages differ from English in many ways, presenting several exciting challenges when developing their respective translation systems. Indian languages often use the Subject Object Verb (SOV) ordering to form sentences, unlike English, which uses Subject Verb Object (SVO) ordering. This difference in sentence structure makes it harder to produce fluent translations; the more words that need to be reordered, the more chance there is to make mistakes when moving them. Tamil, Telugu and Kannada are also highly agglutinative, meaning a single word often includes affixes that represent additional meaning, like tense or number. Fortunately, our research to improve Japanese (an SOV language) translation helped us with the word order challenge, while our work translating languages like German, Turkish and Russian provided insight into the agglutination problem.

You can expect translations for these new alpha languages to be less fluent and include many more untranslated words than some of our more mature languages—like Spanish or Chinese—which have much more of the web content that powers our statistical machine translation approach. Despite these challenges, we release alpha languages when we believe that they help people better access the multilingual web. If you notice incorrect or missing translations for any of our languages, please correct us; we enjoy learning from our mistakes and your feedback helps us graduate new languages from alpha status. If you’re a translator, you’ll also be able to take advantage of our machine translated output when using the Google Translator Toolkit.

Since these languages each have their own unique scripts, we’ve enabled a transliterated input method for those of you without Indian language keyboards. For example, if you type in the word “nandri,” it will generate the Tamil word நன்றி (see what it means). To see all these beautiful scripts in action, you’ll need to install fonts* for each language.

We hope that the launch of these new alpha languages will help you better understand the Indic web and encourage the publication of new content in Indic languages, taking us five alpha steps closer to a web without language barriers.

*Download the fonts for each language: Tamil, Telugu, Bengali, Gujarati and Kannada.

Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths

Posted by Matthias Grundmann, Vivek Kwatra, and Irfan Essa, Research Team

Earlier this year, we announced the launch of new features on the YouTube Video Editor, including stabilization for shaky videos, with the ability to preview them in real-time. The core technology behind this feature is detailed in this paper, which will be presented at the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2011).

Casually shot videos captured by handheld or mobile cameras suffer from significant amount of shake. Existing in-camera stabilization methods dampen high-frequency jitter but do not suppress low-frequency movements and bounces, such as those observed in videos captured by a walking person. On the other hand, most professionally shot videos usually consist of carefully designed camera configurations, using specialized equipment such as tripods or camera dollies, and employ ease-in and ease-out for transitions. Our goal was to devise a completely automatic method for converting casual shaky footage into more pleasant and professional looking videos.



Our technique mimics the cinematographic principles outlined above by automatically determining the best camera path using a robust optimization technique. The original, shaky camera path is divided into a set of segments, each approximated by either a constant, linear or parabolic motion. Our optimization finds the best of all possible partitions using a computationally efficient and stable algorithm.

To achieve real-time performance on the web, we distribute the computation across multiple machines in the cloud. This enables us to provide users with a real-time preview and interactive control of the stabilized result. Above we provide a video demonstration of how to use this feature on the YouTube Editor. We will also demo this live at Google’s exhibition booth in CVPR 2011.

For further details, please read our paper.

Google at CVPR 2011

Posted by Mei Han and Sergey Ioffe, Research Team

The computer vision community will get together in Colorado Springs the week of June 20th for the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2011). This year will see a record number of people attending the conference and 27 co-located workshops and tutorials. The registration was closed at 1500 attendees even before the conference started.

Computer Vision is at the core of many Google products, such as Image Search, YouTube, Street View, Picasa, and Goggles, and as always, Google is involved in several ways with CVPR. Andrew Senior is serving as an area chair of CVPR 2011 and many Googlers are reviewers. Googlers also co-authored these papers:

• Where's Waldo: Matching People in Images of Crowds by Rahul Garg, Deva Ramanan, Steve Seitz, Noah Snavely
• Visual and Semantic Similarity in ImageNet by Thomas Deselaers, Vittorio Ferrari
• Multicore Bundle Adjustment by Changchang Wu, Sameer Agarwal, Brian Curless, Steve Seitz
• A Hierarchical Conditional Random Field Model for Labeling and Segmenting Images of Street Scenes by Qixing Huang, Mei Han, Bo Wu, Sergey Ioffe
• Kernelized Structural SVM Learning for Supervised Object Segmentation by Luca Bertelli, Tianli Yu, Diem Vu, Salih Gokturk
• Discriminative Tag Learning on YouTube Videos with Latent Sub-tags by Weilong Yang, George Toderici
• Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths by Matthias Grundmann, Vivek Kwatra, Irfan Essa
• Image Saliency: From Local to Global Context by Meng Wang, Janusz Konrad, Prakash Ishwar, Yushi Jing, Henry Rowley

If you are attending the conference, stop by Google’s exhibition booth. In addition to talking with Google researchers, you will get to see examples of exciting computer vision research that has made it into Google products including, among others, the following:

• Google Earth Facade Shadow Removal by Mei Han, Vivek Kwatra, and Shengyang Dai
  We will demonstrate our technique for removing shadows and other lighting/texture artifacts from building facades in Google Earth. We obtain cleaner, clearer, and more uniform textures which provide users with an improved visual experience.
• Video Stabilization on YouTube Editor by Matthias Grundmann, Vivek Kwatra, and Irfan Essa
  Casually shot videos captured by handheld or mobile cameras suffer from significant amount of shake. In contrast, professionally shot video usually employs stabilization equipment such as tripods or camera dollies, and employ ease-in and ease-out for transitions. Our technique mimics these cinematographic principles, by optimally dividing the original, shaky camera path into a set of segments and approximating each with either constant, linear or parabolic motion using a computationally efficient and stable algorithm. We will showcase a live version of our algorithm, featuring real-time performance and interactive control, which is publicly available at youtube.com/editor.
• Tag Suggest for YouTube by George Toderici and Mehmet Emre Sargin
  YouTube offers millions of users the opportunity to upload videos and share them with their friends. Many users would love to have their videos discoverable but don't annotate them properly. One new feature on YouTube that seeks to address this problem is tag prediction based on video content and independently based on text metadata.

6/17/2011 UPDATE: "Posted by" was changed to include Sergey Ioffe.

Our first round of Google Research Awards for 2011

Posted by Maggie Johnson, Director of Education & University Relations

We’ve just finished awarding the latest round of Google Research Awards, which provide funding to full-time faculty working on research in areas of mutual interest with Google. A record number of submissions came in this round, and we are delighted to be funding 112 awards across 21 different focus areas for a total of more than $6.75 million. The subject areas that received the highest level of support were systems and infrastructure, human computer interaction, Geo/maps and machine learning. Thanks to strong international collaborations, 23% of the funding in this round was awarded to universities outside the U.S.

In prior years, we’ve used this blog post to highlight some of our top-ranked projects, but this year, we’d like to give you an inside look into how we determine the award recipients.

Designating the awards involves a careful and detailed review process. First, we have a set of internal research leads, each a well-known expert in their field, review all the proposals in their area. They assess the proposals on merit, innovation, connection to Google’s products and services and fit with our overall research agenda. The research leads then assign several volunteer reviewers—culled from experts on their team or other Google engineers holding PhDs—to weigh each proposal.

All these reviews are recorded in an internal grant administration system, and the research leads make their funding recommendations. These recommendations are aggregated and a series of committee meetings are run, one for each research area. The research lead attends, along with members of the university relations team and executives in research. This committee reviews each proposal that the research lead has recommended for funding, using the same criteria mentioned above. This additional review process may change the proposal rankings and sometimes brings back other proposals for reconsideration.

Once the committee meetings are complete, we make the final funding decisions, which are based on the available budget and balancing the funding across research areas and geographic regions. The final decisions are reviewed one last time by research management, and then we distribute the awards to the selected faculty.

As the number of submissions for these research awards continues to grow, we remain committed to a merit-based review process with effective checks and balances. Congratulations to the well-deserving recipients of this round’s awards, and if you are interested in applying for the next round (deadline is August 1), please visit our website for more information.

Instant Mix for Music Beta by Google

Posted by Douglas Eck, Research Scientist

Music Beta by Google was announced at the Day One Keynote of Google I/O 2011. This service allows users to stream their music collections from the cloud to any supported device, including a web browser. It’s a first step in creating a platform that gives users a range of compelling music experiences. One key component of the product, Instant Mix, is a playlist generator developed by Google Research. Instant Mix uses machine hearing to extract attributes from audio which can be used to answer questions such as “Is there a Hammond B-3 organ?” (instrumentation / timbre), “Is it angry?” (mood), “Can I jog to it?” (tempo / meter) and so on. Machine learning algorithms relate these audio features to what we know about music on the web, such as the fact that Jimmy Smith is a jazz organist or that Arcade Fire and Wolf Parade are similar artists. From this we can predict similar tracks for a seed track and, with some additional sequencing logic, generate Instant Mix playlists from songs in a user’s locker.

Because we combine audio analysis with information about which artists and albums go well together, we can use both dimensions of similarity to compare songs. If you pick a mellow track from an album, we will make a mellower playlist than if you pick a high energy track from the same album. For example, here we compare short Instant Mixes made from two very different tracks by U2. The first Instant Mix comes from "Mysterious Ways," an upbeat, danceable track from Achtung Baby with electric guitar and heavy percussion.

1. U2 "Mysterious Ways"
2. David Bowie "Fame"
3. Oingo Boingo "Gratitude"
4. Infectious Grooves “Spreck”
5. Red Hot Chili Peppers “Special Secret Song Inside”

Compare this to a short Instant Mix made from a much more laid back U2 cut, "MLK" from the album Unforgettable Fire. This track has delicate vocals on top of a sparse synthesizer background and no percussion.

1. U2 "MLK"
2. Jewel “Don’t”
3. Antony and the Johnsons “What Can I Do?”
4. The Beatles “And I Love Her”
5. Van Morrison “Crazy Love”

As you can hear, the “Mysterious Ways” Instant Mix is funky, with strong percussion and high-energy vocals while the “MLK” mix carries on with that track's laid-back lullaby feeling.

Our approach also allows us to create mixes from music in the long tail. Are you the lead singer in an unknown Dylan cover band? Even if your group is new or otherwise unknown, Instant Mix can still use audio similarity to match your tracks to real Dylan tracks (provided, of course, that you sing like Bob and your band sounds like The Band).

Our goal with Instant Mix is to build awesome playlists from your music collection. We achieve this by using machine learning to blend a wide range of information sources, including features derived from the music audio itself. Though we’re still in beta, and still have a lot of work to do, we believe Instant Mix is a great tool for music discovery that stands out from the crowd. Give it a try!

Further reading by Google Researchers:
Machine Hearing: An Emerging Field
Richard F. Lyon.

Sound Ranking Using Auditory Sparse-Code Representations
Martin Rehn, Richard F. Lyon, Samy Bengio, Thomas C. Walters, Gal Chechik.

Large-Scale Music Annotation and Retrieval: Learning to Rank in Joint Semantic Spaces
Jason Weston, Samy Bengio, Philippe Hamel.

After the award: students and mentors

Posted by Leslie Yeh Johnson, University Relations Manager

For the past two years, we’ve looked forward to honoring the best and the brightest graduate students pursuing doctoral degrees around the globe through Google’s fellowship program. We’re thrilled to be supporting these students with a monetary gift, but what happens after the awards are given out?

An important component of our fellowship program is the Google research mentor. Each fellowship student is paired with a full-time Googler based on mutual research interests. The idea is that the mentors provide a different point of view from the students’ day-to-day academic world, introduce them to a professional network which will last their entire career and provide meaningful context and feedback based on their own experiences as the students work their way through graduate school. In return, the Googler has the unique opportunity to mentor one of the top students in the field and foster a future leader in technology.

Jason Mars of the University of Virginia was awarded the 2010 Google U.S./Canada Fellowship in Compiler Technology. Robert Hundt, a compiler and datacenter researcher at Google, was Jason’s research mentor. Here, they share how a fellowship turned out to be much more than just an award:

In Jason’s words
When I first met Robert at a research conference, I didn’t realize he would become one of the most important mentors I've had. Beyond our match in personality and thinking styles, Robert took an interest in shaping and sharpening me as a researcher and engineer, and I've benefited greatly from his guidance. I realized after my first internship that Google faces some of the most compelling research problems in computer science today. Robert’s mentorship combined with my Google Fellowship have prompted me to delve deeper into these open problems. In fact, I ended up returning to Google for two subsequent internships. Together, Robert and I have published a number of research papers (with more on the way) and filed two Google patent applications. Our relationship is greater than just mentor and mentee—we are colleagues and friends.

In Robert’s words
Given the high expectations we have for our interns, it’s no surprise to me that one of our most successful interns, Jason Mars, is a recipient of an esteemed Google Fellowship. A three-time returning intern, Jason brought great levels of enthusiasm, creativity, problem solving and problem finding skills to our team, and kept us all on our toes by challenging assumptions and the status quo. He has written half a dozen conference and workshop papers and has built relationships with many people, not just at Google, but throughout Silicon Valley. Jason is well on his way to become a renowned expert in datacenter performance and contention issues. I am very proud of him and grateful to be part of his journey. I believe I may have learned as much from him, with his limitless energy and technical creativity, as he has learned during his time at Google. Lastly, and most importantly, I have won a friend.

2011 Google Fellowships
This year, we we will be awarding fellowships to 34 promising young research students around the globe. These awards support their tuition and stipend, as well as a Google research mentor. Click here for a PDF of all of our Google Fellowship recipients. Congratulations to all the fellows; we look forward to seeing you move technology forward.

Google Scribe: Now with automatic text for links and faster formatting options

Posted by Kartik Singh and Kuntal Loya, Google Scribe team

Since Google Scribe's first release on Google Labs last year, we have been poring over your feedback and busy adding the top features you asked for. Today, we're excited to announce a new version of Google Scribe that brings more features to word processing.

Besides formatting, Google Scribe provides features that help you author high quality documents quickly:

1. Automatic text for links
   Adding a hyperlink to your document has been a two-step process of choosing the link and the text to display for it. Google Scribe now makes it easier. Just paste or type any link into your document and Google Scribe will set an appropriate link text.
   
   
2. Smart toolbar
   Do you repeatedly spend time reaching out to the toolbar to format your document ? To speed-up formatting, Google Scribe now displays an abridged toolbar close-by when you select a portion of the document.
   
   
   
   
3. Text completion in 12 languages
   Google Scribe auto-completes text as you type. In addition to saving keystrokes, the suggestions indicate correct or popular phrases to use. Google Scribe now auto-detects document language, so you no longer need to choose a language.
   
   
   You can view other applicable suggestions by clicking on the options button next to the Google Scribe icon and choosing “Show Multiple Suggestions”.
   
   
   We have extended auto-complete support to Arabic, Dutch, French, German, Hungarian, Italian, Polish, Portuguese, Russian, Spanish and Swedish in addition to English that we already supported.
   
   
4. Correct your document as you type
   Google Scribe now has basic support for checking spelling, punctuation and phrases in your document. Google Scribe underlines incorrect usage and clicking on underlined words or phrases will display a menu of suggested corrections to choose from.
   
   
   We are continuously working on expanding the list of proofreading features. Stay tuned.

Try out the new Google Scribe at scribe.googlelabs.com and let us know what you think.

Google at ACL 2011

Posted by Ryan McDonald and Fernando Pereira, Research Team

The Annual Meeting of the Association for Computational Linguistics is one of the premier conferences for language and text technologies. Many employees at Google have strong roots in the community of researchers that attend this meeting, including many of our researchers working on machine translation and speech.

At this years conference, Google is particularly well represented. The General Chair is Dekang Lin and a few Googlers are serving as technical Area Chairs (in addition to the plethora of Googlers that reviewed papers for the conference). Google is also a Platinum Sponsor of ACL this year.

Research advances at Google can be seen throughout the conference’s technical content. Below is a complete list of Googler-authored or co-authored papers in the main conference. We want to give special emphasis to this year’s best paper award, given to “Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections” by CMU graduate student and Google intern Dipanjan Das and his internship advisor Slav Petrov. ACL is an extremely selective conference and this award speaks volumes to the importance of syntactic analysis and using bilingual corpora to project syntactic resources from resource rich languages (like English) to other languages. Congratulations Dipanjan and Slav!

Googlers are also involved in two of this year’s tutorials. Marius Pasca will present “Web Search Queries as a Corpus” and Kuzman Ganchev and his colleagues will teach about “Rich Prior Knowledge in Learning for Natural Language Processing”. Finally, Katja Filippova and her colleagues are running a workshop on “Monolingual Text-to-Text Generation”.

ACL will take place this year in Portland from June 19th to June 24th.

Papers by Googlers (a * indicates a paper that will be linked to after the conference):

Ranking Class Labels Using Query Sessions*
Marius Pasca

Fine-Grained Class Label Markup of Search Queries*
Joseph Reisinger and Marius Pasca

Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections
Dipanjan Das and Slav Petrov

Large-Scale Cross-Document Coreference Using Distributed Inference and Hierarchical Models
Sameer Singh, Amarnag Subramanya, Fernando Pereira and Andrew McCallum

Piggyback: Using Search Engines for Robust Cross-Domain Named Entity Recognition
Stefan Rüd, Massimiliano Ciaramita, Jens Müller and Hinrich Schütze

Beam-Width Prediction for Efficient Context-Free Parsing
Nathan Bodenstab, Aaron Dunlop, Keith Hall and Brian Roark

Language-independent compound splitting with morphological operations
Klaus Macherey, Andrew Dai, David Talbot, Ashok Popat and Franz Och

Model-Based Aligner Combination Using Dual Decomposition
John DeNero and Klaus Macherey

Binarized Forest to String Translation
Hao Zhang, Licheng Fang, Peng Xu and Xiaoyun Wu

Semi-supervised Latent Variable Models for Fine-grained Sentiment Analysis
Oscar Tackstrom and Ryan McDonald

Make beautiful interactive maps even faster with new additions to the Fusion Tables API

Posted by Rebecca Shapley, Jayant Madhavan, Rod McChesney, and Kathryn Hurley, Fusion Tables team

Google Fusion Tables is a modern data management and publishing web application that makes it easy to host, manage, collaborate on, visualize, and publish data tables online. Since we first launched Fusion Tables almost two years ago, we've seen tremendous interest and usage from dozens of areas, from journalists to scientists to open-data entrepreneurs, and have been excited to see the innovative applications that our users have been able to rapidly build and publish.

We've been working hard to enrich what Fusion Tables offers for customization and control of visual presentation. This past fall we added the ability to style the colors and icons of mapped data with a few clicks in the Fusion Tables web app. This spring we made it easy to use HTML and customize what users see in the info window that appears after a click on the map. We’ve enjoyed seeing the impressive visualizations you have created. Some, like the Guardian’s map of deprivation in the UK, were created strictly within the web app, while apps like the Bay Citizen’s Bike Accident tracker and the Texas Tribune’s Census 2010 interactive map take advantage of the Fusion Tables SQL API to do even more customization.


Of course, it’s not always convenient to do everything through a web interface, and today we’re delighted to invite trusted testers to try out the new Fusion Tables Styling and Info Window API. Now developers will be able to set a table’s map colors and info windows with code.

Even better, this new Styling and Info Window API will be part of the Google APIs Console. The Google APIs Console helps you manage projects and teams, provision access quotas, and view analytics and metrics on your API usage. It also offers sample code that supports the OAuth 2.0 client key management flow you need to build secure apps for your users.

So if you've been looking for a way to programmatically create highly-customizable map visualizations from data tables, check out our new APIs and let us know what you think! To become a trusted tester, please apply to join the Google Group and tell us a little bit about how you use the Fusion Tables API.

Google at CHI 2011

Posted by Yang Li, Research Scientist

Cross-posted with the Technical Programs and Events Blog

Google has an increasing presence at ACM CHI: Conference on Human Factors in Computing Systems, which is the premiere conference for Human Computer Interaction research. Eight Google papers will appear at the conference. These papers not only touch on our core areas such as Search, Chrome and Android but also demonstrate our growing effort in new areas where HCI is essential, such as new search user interfaces, gesture-based interfaces and cross-device interaction. They showcase our efforts to address user experiences in diverse situations. Googlers are playing active roles in the conference in many other ways too: participating in conference committees, hosting panels, organizing workshops and teaching courses, as well as running demos and 1:1 sessions at Google's booth.

This year's CHI takes place in Vancouver, BC, from May 7th - 12th.

PAPERS

Gesture Avatar: A Technique for Operating Mobile User Interfaces Using Gestures, by Hao Lü, Yang Li*

User-Defined Motion Gestures for Mobile Interaction by Jaime Ruiz, Yang Li*, Edward Lank

Experimental Analysis of Touch-Screen Gesture Designs in Mobile Environments by Andrew Bragdon, Eugene Nelson, Yang Li*, Ken Hinckley

Many Bills: Engaging Citizens through Visualizations of Congressional Legislation by Yannick Assogba, Irene Ros, Joan DiMicco, Matt McKeon*

YouPivot: Improving Recall with Contextual Search by Joshua Hailpern, Nicholas Jitkoff*, Andrew Warr*, Karrie Karahalios, Robert Sesek, Nik Shkrob

Oops, I Did It Again: Mitigating Repeated Access Control Errors on Facebook by Serge Egelman, Andrew Oates*, Shriram Krishnamurthi

Deep Shot: A Framework for Migrating Tasks Across Devices Using Mobile Phone Cameras by Tsung-Hsiang Chang, Yang Li*

DoubleFlip: A Motion Gesture Delimiter for Mobile Interaction by Jaime Ruiz, Yang Li*

WORKSHOPS

Crowdsourcing and Human Computation: Systems, Studies and Platforms by Michael Bernstein, Ed H. Chi*, Lydia B. Chilton, Björn Hartmann, Aniket Kittur, Robert C. Miller

PANELS

Designing for User Experience: Academia & Industry by Joseph 'Jofish' Kaye, Elizabeth Buie, Jettie Hoonhout, Kristina Höök, Virpi Roto, Scott Jenson*, Peter Wright

Festschrift Panel in Honor of Stuart K. Card by Ed H. Chi*, Peter Pirolli, Bonnie John, Judith S Olson, Dan Russell*, Tom Moran

CHI Should be Replicating and Validating Results More: Discuss by Max L. Wilson, Wendy Mackay, Ed H. Chi*, Michael Bernstein, Dan Russell*, Harold Thimbleby

Transferability of Research Findings: Context-Dependent or Model-Driven by Ed H. Chi*, Mary Czerwinski, David Millen, Dave Randall, Gunnar Stevens, Volker Wulf, John Zimmerman

The Future of Child-Computer Interaction by Allison Druin, Gary Knell, Elliot Soloway, Dan Russell*, Elizabeth Mynatt, Yvonne Rogers

CASE STUDIES

From Basecamp to Summit: Scaling Field Research Across 9 Locations by Jens Riegelsberger*, Audrey Yang*, Konstantin Samoylov*, Elizabeth Nunge*, Molly Stevens*, Patrick Larvie*

COURSES

Design and Analysis of Large Scale Log Studies by Susan Dumais, Robin Jeffries*, Dan Russell*, Diane Tang*, Jaime Teevan

SIG MEETING

Participatory Culture in the Age of Social Media by Dana Rotman, Sarah Vieweg, Sarita Yardi, Ed H. Chi*, Jenny Preece, Ben Shneiderman, Peter Pirolli, Tom Glaisyer

Note: * denotes a Googler