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CoLing 2016
- 1. THE 26TH INTERNATIONAL CONFERENCEON COMPUTATIONAL LINGUISTICS DECEMBER 11-16, 2016 OSAKA, JAPAN
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- 3. STATISTICS • 3rd CoLingin Japan after Tokyo & Kyoto • ~1100 presenters in total • Almost 400 students • 1039 papers submitted • 32% acceptance rate • 337 accepted papers • 135 presentations • 202 posters • ~25% neural network papers
- 4. AREA-WISE ACCEPTANCE RATES AreaTotal submissio ns Total (%) accepted Paraphrasing, Textual Entailment 32 9 (28%) Sentiment Analysis, Computational Argumentation 85 22 (26%) Information Retrieval, Information Extraction, Question Answering 126 26 (21%) Applications 76 28 (37%) Dialog Processing and Dialog Systems, Multimodal Interfaces 29 12 (41%) Speech Recognition, Text-to-Speech, Spoken Language Understanding 24 11 (46%) Machine Translation 88 31 (35%) Resources, Software and Tools 56 25 (45%)
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- 6. QUOTES • Probably thelast PBMT paper ever • People working on digital humanities don't really know what digital humanities are… • Kids learn language having heard a very small amount – to further advance AI we need to focus on low resourced conditions instead of big data • Home Made Restaurant Warmly • to make by hand taste
- 8. HYTRA 6 SIXTH WORKSHOPON HYBRID APPROACHES TO TRANSLATION
- 9. KEYNOTE: MARK SELIGMAN,SPOKEN TRANSLATION, INC. PERCEPTUALLY GROUNDED DEEP SEMANTICS IN FUTURE HYBRID MACHINE TRANSLATION Nine Issues in Speech Translation – Discourse – Speech acts – Topic tracking – Domain – Prosody – Pauses – Pitch, stress – Translation mismatches – System architecture, data structures Improve Statistical MT • User feedback + machine learning • More, better data • Parsing > hybrid MT
- 10. KEYNOTE: MARK SELIGMAN,SPOKEN TRANSLATION, INC. PERCEPTUALLY GROUNDED DEEP SEMANTICS IN FUTURE HYBRID MACHINE TRANSLATION 車 _car を _obj 運転 _driving する _do 人 _person Syntactic structure NP VP Semantic structure PP V N NP VN V drive person person car mod agt obj The Return of Semantics: Interlingua/Ontologies Grounded Semantics
- 11. MAIN CONFERENCE KEYNOTES: JOAKIM NIVRE,REIKO MAZUKA, DINA DEMNER-FUSHMAN, SIMONE TEUFEL
- 12. JOAKIM NIVRE UPPSALA UNIVERSITY,SWEDEN Universal Dependencies - Dubious Linguistics and Crappy Parsing? • Maximize parallelism – but don’t overdo it • Don’t annotate the same thing in different ways • Don’t make different things look the same • Don’t annotate things that are not there • Universal taxonomy with language-specific elaboration • Languages select from a universal pool of categories • Allow language-specific extensions
- 13. JOAKIM NIVRE UPPSALA UNIVERSITY,SWEDEN Manning's law 1. UD needs to be satisfactory on linguistic analysis grounds for individual languages. 2. UD needs to be good for linguistic typology, i.e., providing a suitable basis for bringing out cross-linguistic parallelism across languages and language families. 3. UD must be suitable for rapid, consistent annotation by a human annotator. 4. UD must be suitable for computer parsing with high accuracy. 5. UD must be easily comprehended and used by a non-linguist, whether a language learner or an engineer with prosaic needs for language processing. 6. UD must support well downstream language understanding tasks (relation extraction, reading comprehension, machine translation, …).
- 14. JOAKIM NIVRE UPPSALA UNIVERSITY,SWEDEN Dubious linguistics? • Lexical dependencies and functional relations encoded in a single tree • Grounded in linguistic typology and dependency grammar traditions Crappy parsing? • Not so bad with existing parsers, especially for cross-lingual parsing • Learn richer parsing models grounded in linguistic typology
- 15. REIKO MAZUKA RIKEN BRAINSCIENCE INSTITUTE, JAPAN • 12month old babies are called 'old babies‘ • Medical stuff has lots of data, lots of problems • … let alone … DINA DEMNER-FUSHMAN U.S. NATIONAL LIBRARY OF MEDICINE, U.S.A. SIMONE TEUFEL UNIVERSITY OF CAMBRIDGE, U.K.
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- 17. CHARNER: CHARACTER-LEVEL NAMED ENTITYRECOGNITION Onur Kuru, Ozan Arkan Can, Deniz Yuret • Stacked bidirectional LSTMs • inputs characters • outputs tag probabilities for each character • Probabilities are then converted to word level named entity tags using a Viterbi decoder • Close to state-of-the-art NER performance in seven languages with the same basic model using only labeled NER data and no hand-engineered features or other external resources like syntactic taggers or Gazetteers
- 18. WHAT TOPIC DOYOU WANT TO HEAR ABOUT? A BILINGUAL TALKING ROBOT USING ENGLISH AND JAPANESE WIKIPEDIAS Graham Wilcock, Kristiina Jokinen
- 19. PHRASE-BASED MACHINE TRANSLATION USINGMULTIPLE PREORDERING CANDIDATES Yusuke Oda, Taku Kudo, Tetsuji Nakagawa, Taro Watanabe
- 20. INTERACTIVE ATTENTION FOR NEURALMACHINE TRANSLATION Fandong Meng, Zhengdong Lu, Hang Li, Qun Liu
- 21. INTERACTIVE ATTENTION FOR NEURALMACHINE TRANSLATION Fandong Meng, Zhengdong Lu, Hang Li, Qun Liu • Models the interaction between the decoder and the representation of source sentence during translation by both reading and writing operations • Can keep track of the interaction history and therefore improve the translation performance
- 22. SUB-WORD SIMILARITY BASEDSEARCH FOR EMBEDDINGS: INDUCING RARE-WORD EMBEDDINGS FOR WORD SIMILARITY TASKS AND LANGUAGE MODELLING Mittul Singh, Clayton Greenberg, Youssef Oualil, Dietrich Klakow • Training good word embeddings requires large amounts of data. Out-of-vocabulary words will still be encountered. • Existing methods use computationally-intensive morphological analysis to generate embeddings • The proposed system applies a computationally-simpler sub-word search on words that have existing embeddings • Up to 50% reduction in rare word perplexity in comparison to other more complex language models
- 23. MULTI-ENGINE AND MULTI-ALIGNMENTBASED AUTOMATIC POST-EDITING AND ITS IMPACT ON TRANSLATION PRODUCTIVITY Santanu Pal, Sudip Kumar Naskar, Josef van Genabith • Parallel system combination in the APE stage of a sequential MT- APE combination • Substantial translation improvements • automatic evaluation (+5.9%) • productivity in post-editing (21.76%) • System combination on the level of APE alignments yields further improvements
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- 25. Achieves the state-of-the-artconversion Fscore 95.6
- 31. IMPROVING ATTENTION MODELINGWITH IMPLICIT DISTORTION AND FERTILITY FOR MACHINE TRANSLATION
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- 33. PREDICTING HUMAN SIMILARITYJUDGMENTS WITH DISTRIBUTIONAL MODELS: THE VALUE OF WORD ASSOCIATIONS Simon De Deyne, Amy Perfors, Daniel J Navarro • Internal language models, that are more closely aligned to the mental representations of words • Count based model for text corpora • Predicting structure from text corpora using word embeddings • Count based model for word associations • A spreading activation approach to semantic structure
- 34. EXTENDING THE USEOF ADAPTOR GRAMMARS FOR UNSUPERVISED MORPHOLOGICAL SEGMENTATION OF UNSEEN LANGUAGES Ramy Eskander, Owen Rambow, Tianchun Yang • Segmentation of words in a language into a sequence of morphs • Without rewriting or normalizing morphs • Without identifying the stem • Without identifying morphological features
- 35. KEYSTROKE DYNAMICS AS SIGNALFOR SHALLOW SYNTACTIC PARSING Barbara Plank • Runnuer-up for best paper
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- 37. 2018 COLING 2018 • SantaFe, New Mexico, USA • August 20-25, 2018 LREC 2018 • Miyazaki, Japan • May 7-12, 2018
- 38. LESS COLING &MORE OSAKA HTTP://LIELAKEDA.LV HTTP://EJ.UZ/COLING2016