Supporting Communications in Global Networks. Kevin Duh & 歐陽靖民

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1 Supporting Communications in Global Networks Kevin Duh & 歐陽靖民

2 Supporting Communications in Global Networks Machine Translation Kevin Duh

3 6000 Number of Languages in the World 世界中の言語の数

4 Image courtesy of: nasa.gov Transportation Telecommunications Translation

5 There are 6000 languages in the world. MACHINE TRANSLATION 機械翻訳 世界には 6000 の言語があります

6 Image courtesy: Biographical Memoirs of National Academy of Science, vol. 57 When I look at an article in Russian, I say: This is really written in English, but has been coded in some strange symbols. I will now proceed to decode. [1947] Warren Weaver, American scientist ( )

7 Rule-Based Machine Translation (1960 now) Manual encoding of linguistic knowledge into rules Data-Driven Machine Translation (2000 now) Exploiting Statistics from Large Data

8 Architecture of Data-Driven Machine Translation There are 6000 languages in the world Bilingual Text Data 対訳データ Statistics 統計的学習 Probabilistic Models 確率モデル 世界には 6000 の言語があります

9 Architecture of Data-Driven Machine Translation There are 6000 languages in the world Word Translation Model あります 6000 言語には世界 世界 には 6000 の言語 が あります Fluency Model

10 How to build these models?

11 Exercise: Let s build one now!

12 1a) evas dlrow-eht 1b) 2a) dlrow-eht si detcennoc 2b) 3a) hcraeser si tnatropmi 3b) 4a) ew eb-ot-mia tseb ni dlrow-eht 4b)

13 1a) evas dlrow-eht 1b) dlrow-eht Frequency 3 2a) dlrow-eht si detcennoc 2b) 3a) hcraeser si tnatropmi 3b) 4a) ew eb-ot-mia tseb ni dlrow-eht 4b) dlrow-eht si si 1 2 1

14 1a) save the-world 1b) the-world Frequency 3 2a) the-world is connected 2b) 3a) research is important 3b) 4a) we aim-to-be best in the-world 4b) the-world is is 1 2 1

15 1a) save the-world 1b) 世界を救え 世界 the-world Frequency 3 2a) the-world is connected 2b) 世界は繋がってる 3a) research is important 3b) 研究は大事 を救え the-world は is 繋がってる is a) we aim-to-be best in the-world 4b) 世界一を狙う

16 in practice, it s not that easy

17 Recent Research Efforts Duh+(2010), Analysis of Translation Model Adaptation Duh+(2011), Bayesian Adaptation of Alignment Matrices What if data is Other Data Other Data Other Data insufficient? Bilingual Text Data 対訳データ Statistics 統計的学習 Probabilistic Models 確率モデル

18 Recent Research Efforts English: Japanese: Subject 主語 Verb 目的語 Object 動詞 Visit Posters 5-7 for more info How to use Japanese-specific knowledge? Bilingual Text Data 対訳データ Statistics 統計的学習 Probabilistic Models 確率モデル

19 Recent Research Efforts Probabilistic Model as Finite State Transducer Can we translate faster? Bilingual Text Data 対訳データ Statistics 統計的学習 Probabilistic Models 確率モデル Using technology from Speech Recognition: Hori+(2007), Efficient WFST one-pass decoding w/ on-the-fly rescoring

20 Killer Application for Translation?

21 Three General Scenarios translate in translate out translate 2-way

22 translate in The Future Knowledge Worker Translation Technical Articles Breaking News Social Media Robert Brook

23 translate out The entire world is your market. Export Things: Cars Electronics Machinery Steel Chemicals Export Culture: Movies Manga Apps Music

24 2-way Real-time Conversation now ー翻訳中ー 6000 言語の数 smartphone

25 How will you use translation technology? あなたなら 翻訳をどのようなコミュニケーションに使う?

26

27 Supporting Communication in Global Networks Social Network Mining 歐陽靖民

28 Japanese Programmers on GitHub Created by

29

30 Six Degree of Separation Stanley Milgram American Social Psychologist BBC recreating the experiment in 2009 (From Nairobi to Boston)

31 Obesity and Social Network (Christakis and Fowler, 2007) Happiness and Social Network (Fowler and Christakis, 2008)

32 The Age of Online Social Networking 500,000,000 Active Users 30 billion content shared 200,000,000 Users 65 million tweets per day

33 Social Network Mining

34 Why social network mining? Identifying influential users in a network

35 Why social network mining? Understanding how information spreads ^.^ I heard that it is good! ^_^ I like this!

36 Why social network mining? Making Social Recommendations 30 of your best friends have already bought this!

37 Understanding Social Networks

38 Measuring a Social Network Example: Studying Twitter Four Degree of Separation in Twitter Kwak et al. What is Twitter, a Social Network or a News Media? WWW, 2010.

39 Modeling a Social Network Exploring complex networks (Strogatz, 2001) Small World Networks Scale-free Networks Emergence of scaling in random networks (Barabási & Albert, 1999)

40 Supporting Users in Social Networks

41 Social Information Overloading

42 Supporting Users in Social Networks Learning the relations among people Many interactions / Similar Opinions Few interactions / Different opinions

43 Users participating in the social network New algorithms for supporting the social network Measuring and Modelling the social network

44 Our Research

45 Research Question How trust relations shape user opinions? How trust can be used to predict user opinions?

46

47

48 Trust Relations U1 U2 U1 U1 U3 U2 Timeline U1 U2 U3 Product Ratings

49 Difference in ratings decreases before and after trust Similarity between users increases before and after trust

50 Estimating strength of relations + Predicting user ratings Does he/she like this type of product?? = +???? Influence from different trusted friends

51 Summary

52 Supporting Communication in Global Networks

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