A presentation about the 17th International Conference on Intelligent Text Processing and Computational Linguistics in Konya, Turkey, April 3 - 9, 2016
3. Mevlana University
• Funded in 2009
• Rebuilt from a shopping mall
• Wants to partner-up with other universities
4. CICLing 2016 Statistics
• 206 accepted papers
• 88 for Springer Lecture Notes in Computer Science
• 20 for Journal Computación y Sistemas
• 12 for Journal Polibits
• 20 for International Journal of Computational Linguistics and Applications
• 24 for IEEE CPS proceedings of ACLing (only papers on Arabic language)
• 13 for IEEE CPS proceedings of TurCLing (only papers on Turkic languages)
• 29 for Journal Research in Computing Science
5. CICLing 2016 Statistics
• 157 attended
• 43 with a CICLing oral presentation
• 38 with a ACLing or TurCLing oral presentation
• 76 with a poster for CICLing, ACLing or TurCLing
6. CICLing 2016 Facts
• You are a professor!
• CICLing has no sponsors
• CICLing has no backing organization
• CICLing costs more than other conferences
• CICLing has 3 full-day excursions
7. CICLing 2016 Quotes
• «Some poster presenters came without a poster, because only the
one minute long presentation about the poster was important, so
that they can say that they gave a presentation at the conference»
• «Do you use Theano? No, I don`t have access to GPUs»
• «We use available parallel corpora which is becoming increasingly
available»
• «RNNs are chosen for the wrong reasons: easier to get a paper
published»
9. A Roadmap towards Machine Intelligence
Tomas Mikolov, Facebook Research
Recurrent networks and beyond
• RNNs are chosen for the wrong reasons:
easier to get a paper published
• Optimize not how good something can be learned, but how fast
something new can be learned with as little as possible teaching
• https://www.youtube.com/watch?v=FUlTjKL-mVA
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13. NewsReader: a machine for reading massive streams of
news to generate event-centric knowledge graphs
Piek Vossen, University Amsterdam
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17. Discussion about reproducible results
and publishing of code and data
• Scientists need to advance science
• Not just them selves
• Negative results should also be published whet possible
18. Deeper summarisation: the second time around.
An overview and some practical suggestions
Simone Teufel, University of Cambridge
19. Robots With Heart
Pascale Fung, Hong Kong University of Science and Technology
• «Some people just like to abuse robots»
• Humor is
• Setup
• Trigger
• Punchline
• Robotics people study dancers to learn how to express emotion
through motion
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23. Discussion about robots
How to make future robots and humans get along
• Reward them for good work like children
• Be good ourselves so that they learn good examples
• Keep some kind of backup stop or self-destruct button
• Hardcode only because robots that can learn are too dangerous
25. Generating Bags of Words
from the Sums of their Word Embeddings
Lyndon White, Roberto Togneri, Wei Liu and Mohammed Bennamoun
Best student paper
• NP-Hard problem
• Using a greedy algorithm to convert the vector to a bag of words
• Part-way step towards generating full sentences
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27. Pluralising Nouns in isiZulu and Related Languages
Joan Byamugisha, C. Maria Keet and Langa Khumalo
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29. Pluralising Nouns in isiZulu and Related Languages
Joan Byamugisha, C. Maria Keet and Langa Khumalo
30. Enabling Medical Translation for Low-Resource Languages
Ahmad Musleh, Nadir Durrani, Irina Temnikova, Preslav Nakov, Stephan Vogel and Osama Alsaad
• Data Collection
• Wiki Dumps
• Wikipedia
• Wiktionary
• OmegaWiki
• Doctor-Patient YouTube Videos and Movie Subtitles
• OCR
• Translated into Hindi using Google Translate, and post-edited by a Hindi native speaker
• BabelNet and MeSH
• extracted medical terms from BabelNet
• Medical Subject Headings
• Data Synthesis
31. Enabling Medical Translation for Low-Resource Languages
Ahmad Musleh, Nadir Durrani, Irina Temnikova, Preslav Nakov, Stephan Vogel and Osama Alsaad
• +3.11 BLEU points absolute for English-to-Hindi
• +2.07 for Hindi-to-English
• In future work
• collect more data for Hindi, but also to synthesize Urdu data
• develop a system for Nepali-English
• add Automatic Speech Recognition (ASR) and Speech Synthesis components
in order to build a fully-functional speech-to-speech system
36. Instant Translation Model
Adaptation by Translating Unseen
Words in Continuous Vector Space
The method exploits a projection of semantic representations
of OOV words in the source-language onto the target-language
semantic space to look for translation candidates for the OOV
words
