English Acquisition IAk, IIA fclsl.hi.h.kyoto-u.ac.jp/.../KIT-11A/L10-slides-EA1Ak2Af.pdf2. 正解の解説休憩5分後半40分 • TEDを使った聴き取り訓練の2回目 (L10)

2011/06/28 (火)

English Acquisition IAk, IIAf, 2011第10回 (全13回)

黒田航 (非常勤)

Thursday, June 30, 2011

講義資料のWebページ

✤ URL✤ http://clsl.hi.h.kyoto-u.ac.jp/~kkuroda/lectures.html

✤予習や復習に使って下さい✤解答もこのページから入手可能


http://clsl.hi.h.kyoto-u.ac.jp/~kkuroda/lectures.html

http://clsl.hi.h.kyoto-u.ac.jp/~kkuroda/lectures.html

ボーナス試験

✤最期の授業は任意参加のボーナス試験です✤ 出席回数の足りない人は任意でないです

✤授業でやったのと同じ課題を行なう✤ ハズレがアタリに✤ アタリはアタリのまま


任意参加でない人たち

✤次の方々は今のままではFです✤ EA1Ak

✤ 中島裕貴

✤ EA2Af✤ 原将樹, 西河拓哉, 武藤弘平, 藤本拡二


本日の予定

✤ 前半30分1. L9の聞き取り課題の結果の報告

2. 正解の解説

✤ 休憩5分✤ 後半40分

• TEDを使った聴き取り訓練の2回目 (L10)

• Laurie Santos: Monkey Economy as Irrational as Ours

• テーマ: 比較心理学，意思決定論，経済学


L9の結果 (TED- Deb Roy The birth of a

word から)


L9の得点分布 1Ak,2Af

✤ 参加者: 46人

✤ 平均点: 61.28; 標準偏差: 10.09

✤ 最高点: 88.33; 最低点: 41.07

✤ n = 56

✤ 得点グループ

✤ 65点後半が中心のグループ

✤ 85点後半が中心のグループ?


L9の得点分布 1Ak

✤ 受講者数: 28

✤ 平均点: 34.63/n [61.83] 点

✤ 標準偏差: 4.50/n [8.03] 点

✤ 最高点: 45.00/n [80.36] 点

✤ 最低点: 25.00/n [44.64] 点

✤ n = 56


L9の得点分布 2Af

✤ 受講者数: 18

✤ 平均点: 32.47/n [57.98] 点

✤ 標準偏差: 6.17/n [11.01] 点

✤ 最高点: 45.50/n [81.25] 点

✤ 最低点: 23.00/n [41.07] 点

✤ n = 56


平均得点の履歴


L9の正解率分布 1Ak,2Af


✤ 平均: 0.72; 標準偏差: 0.08

✤ 最高: 0.88; 最低: 0.47

✤ 正答率のグループ

✤ 0.7後半が中心のグループ


L9の正答率分布 1Ak


✤ 平均: 0.72; 標準偏差: 0.06

✤ 最高: 0.82; 最低: 0.58


✤ 0.7が中心のグループ


L9の正答率分布 2Af


✤ 平均: 0.72; 標準偏差: 0.10

✤ 最高: 0.88; 最低: 0.47


✤ 0.8が中心のグループ


平均正解率の履歴


L9の解答 (FLP)


誤りの傾向✤ 1. at✤ 2. welcome ⇒ walking,

open✤ 3. did ⇒ get✤ 4. me ⇒ mean✤ 5. leaving ⇒ living✤ 6. freeze ⇒ free✤ 7. see✤ 8. call ⇒ go✤ 9. when✤ 10. here’s✤ 11. off ⇒ often, ask✤ 12. power✤ 13. it✤ 14. with✤ 15. wordscape ⇒

wordscapes✤ 16. for

✤ 17. landscape ⇒ wordscape

✤ 18. peering ⇒ pearing, appearing

✤ 19. people✤ 20. following ⇒ phone,

form✤ 21. take✤ 22. turn✤ 23. same✤ 24. satellite ⇒ all✤ 25. feeds ⇒ series✤ 26. magic✤ 27. looking✤ 28. except✤ 29. are ⇒ relate✤ 30. sphere ⇒

experience, experiment

✤ 31. gives ⇒ keeps✤ 32. a ⇒ eight, 8✤ 33. lives ⇒ leaves, was✤ 34. third ⇒ three✤ 35. rendered ⇒

around, learning, landing

✤ 36. that✤ 37. if ⇒ for, free✤ 38. that ⇒ at, not✤ 39. living✤ 40. into✤ 41. finding✤ 42. fan-out ⇒ final✤ 43. address ⇒ brass,

grass, adress✤ 44. remarkable✤ 45. pulse ⇒ pose, ports

✤ 46. collect ⇒ correct✤ 47. dynamics ⇒

dinamics, dyinamics, dynamix

✤ 48. profound ⇒ found✤ 49. reflect ⇒ flight,

fight✤ 50. gonna✤ 51. take✤ 52. encouraging ⇒

encourage, in-✤ 53. realizes ⇒ low✤ 54. kicks ⇒ keeps✤ 55. back✤ 56. walking ⇒ walking


01/11

✤ But that's looking [1. at] the speech context. What about the visual context? We’re now looking at— think of this as a dollhouse cutaway of our house. We’ve taken those circular fish-eye lens cameras, and we've done some optical correction, and then we can bring it into three-dimensional life. So [2. welcome] to my home. This is a moment, one moment captured across multiple cameras. The reason we [3. did] this is to create the ultimate memory machine, where you can go back and interactively fly around and then breathe video life into this system. What I'm going to do is give you an accelerated view of 30 minutes, again, of just life in the living room. That’s [4. me] and my son on the floor. And there’s video analytics that are tracking our movements. My son is [5. leaving] red ink, I am leaving green ink. We're now on the couch, looking out through the window at cars passing by. And finally, my son playing in a walking toy by himself.


02/11

✤ Now we [6. freeze] the action, 30 minutes, we turn time into the vertical axis, and we open up for a view of these interaction traces we’ve just left behind. And we [7. see] these amazing structures--- these little knots of two colors of thread, we call social hot spots. The spiral thread, we [8. call] a solo hot spot. And we think that these affect the way language is learned. What we’d like to do is start understanding the interaction between these patterns and the language that my son is exposed to to see if we can predict how the structure of when words are heard affects [9. when] they’re learned —so in other words, the relationship between words and what they’re about in the world.

✤ So [10. here’s] how we’re approaching this. In this video, again, my son is being traced out. He's leaving red ink behind. And there's our nanny by the door.


03/11

✤ Nanny: You want water?✤ Baby: Aaaa.)✤ Nanny: All right.✤ (Baby: Aaaa.)✤ She offers water, and [11. off] go the two worms over to the kitchen to

get water. And what we’ve done is use the word “water” to tag that moment, that bit of activity. And now we take the [12. power] of data and take every time my son ever heard the word “water” and the context he saw [13. it] in, and we use it to penetrate through the video and find every activity trace that co-occurred [14. with] an instance of water. And what this data leaves in its wake is a landscape. We call these wordscapes. This is the [15. wordscape] for the word water, and you can see most of the action is in the kitchen. That’s where those big peaks are over to the left.


04/11

✤ And just [16. for] contrast, we can do this with any word. We can take the word “bye” as in “good bye.” And we’re now zoomed in over the entrance to the house. And we look, and we find, as you would expect, a contrast in the [17. landscape] where the word “bye” occurs much more in a structured way. So we’re using these structures to start predicting the order of language acquisition, and that’s ongoing work now.

✤ In my lab, which we’re [18. peering] into now, at MIT —this is at the media lab. This has become my favorite way of video graphing just about any space. Three of the key [19. people] in this project, Philip DeCamp, Rony Kubat and Brandon Roy are pictured here. Philip has been a close collaborator on all the visualizations you’re seeing.


05/11

✤ And Michael Fleischman was another Ph.D. student in my lab who worked with me on this home video analysis, and he made the [20. following] observation: that “just the way that we’re analyzing how language connects to events which provide common ground for language, that same idea we can [21. take] out of your home, Deb, and we can apply it to the world of public media.” And so our effort took an unexpected [22. turn].

✤ Think of mass media as providing common ground and you have the recipe for taking this idea to a whole new place. We’ve started analyzing television content using the [23. same] principles —analyzing event structure of a TV signal— episodes of shows, commercials, all of the components that make up the event structure. And we’re now, with [24. satellite] dishes, pulling and analyzing a good part of all the TV being watched in the United States. And you don't have to now go and instrument living rooms with microphones to get people’s conversations, you just tune into publicly available social media [25. feeds]. So we’re pulling in about three billion comments a month. And then the [26. magic] happens.


06/11

✤ You have the event structure, the common ground that the words are about, coming out of the television feeds; you’ve got the conversations that are about that, those topics; and through semantic analysis —and this is actually real data you’re [27. looking] at from our data processing— each yellow line is showing a link being made between a comment in the wild and a piece of event structure coming out of the television signal. And the same idea now can be built up. And we get this wordscape, [28. except] now words are not assembled in my living room. Instead, the context, the common ground activities, are the content on television that’s driving the conversations. And what we’re seeing here, these skyscrapers now, are commentary that [29. are] linked to content on television. Same concept, but looking at communication dynamics in a different, very different [30. sphere].


07/11

✤ And so fundamentally, rather than, for example, measuring content based on how many people are watching, this [31. gives] us the basic data for looking at engagement properties of content. And just like we can look at feedback cycles and dynamics in, in a, in a family, we can now open up the same concepts and look at, uh, much larger groups of people. This is [32. a] subset of data from our database —just 50,000 out of several million— and the social graph that connects them through publicly available sources. And if you put them on one plain, a second plain is where the content [33. lives]. So we have the programs and the, the, the sporting events and the commercials, and all of the link structures that tie (up) them together make a content graph. And then the important [34. third] dimension. Each of the links that you’re seeing [35. rendered] here is an actual connection made between something someone said and a piece of content. And there are, again, now tens of millions of these links [36. that] give us the connective tissue of social graphs and how they relate to content. And we can now start to probe the structure in interesting ways.


08/11

✤ So [37. if] we, for example, trace the path of one piece of content that drives someone to comment on it, and then we follow where that comment goes, and then look at the entire social graph that becomes activated and then trace back to see the relationship between [38. that] social graph and content, a very interesting structure becomes visible. We call this a co-viewing clique, a virtual [39. living] room if you will. And there are fascinating dynamics at play. It’s not one way. A piece of content, an event, causes someone to talk. They talk to other people. That drives tune-in behavior back [40. into] mass media, and you have these cycles that drive the overall behavior.

✤ Another example —very different— another actual person in our database— and we’re [41. finding] at least hundreds, if not thousands, of these. We’ve given this person a name. This is a pro-amateur, or pro-am, media critic who has this high [42. fan-out] rate. So a lot of people are following this person— very influential —and they have a propensity to talk about what’s on TV. So this person is a key link in connecting mass media and social media together.


9/11

✤ One last example from this data: Sometimes it’s actually a piece of content that is special. So if we go and look at this piece of content, President Obama’s State of the Union [43. address] from just a few weeks ago, and look at what we find in, in this same data set, at the same scale, the engagement properties of this piece of content are truly [44. remarkable]. A nation exploding in conversation in real time in response to what’s on, on the broadcast. And of course, through all of these lines are flowing unstructured language. We can X-ray and get a real-time [45. pulse] of a nation, real-time sense of the social reactions in the different circuits in the social graph being activated by content.

✤ So, to summarize, the idea is this: As our world becomes increasingly instrumented and we have the capabilities to [46. collect] and connect the dots between what people are saying and the context they’re saying it in, what’s emerging is an ability to see new social structures and [47. dynamics] that have previously not been seen.


10/11

✤ It’s like building a microscope or telescope and revealing new structures about our own behavior around communication. And I think the implications here are [48. profound], whether it’s for science, for commerce, for government, or perhaps most of all, for us as individuals. And so just to return to my son, when I was preparing this talk, he was looking over my shoulder, and I showed him the clips I was going to show to you today, and I asked him for permission —granted. And then I went on to [49. reflect], “Isn’t it amazing, this entire database, all these recordings, I’m going to hand off to you and to your sister?” who arrived two years later. “And you guys are going to be able to go back and re-experience moments that you could never, with your biological memory, possibly remember the way you can now.” And he was quiet for a moment. And I thought, “What am I thinking? He’s five years old. He’s not [50. gonna] understand this.” And just as I was having that thought, he looked up at me and said, “So, that when I grow up, I can show this to my kids?” And I thought, “Wow, this is— this is powerful stuff.”


11/11

✤ So I want to leave you with one last memorable moment from our family. This is our— the first time our son took more than two steps at once —captured on film. And I really want you to focus on something as, as I [51. take] you through. It’s a cluttered environment; it’s natural life. My mother’s in the kitchen, cooking, and, of all places, in the hallway, I realize he’s about to do it, about to take more than two steps. And so you hear me [52. encouraging] him, realizing what’s happening, and then the magic happens. Listen very carefully. About three steps in, he [53. realizes] something magic is happening. And the most amazing feedback loop of all [54. kicks] in, and he takes a breath in, and he whispers “wow” and instinctively I echo, I echo back the same. And so let’s fly [55. back] in time to that memorable moment.

✤ DR: Hey. Come here. Can you do it? Oh, boy. Can you do it?✤ Baby: Yeah.✤ DR: Ma, he’s [56. walking].✤ (Laughter) (Applause) Thank you. (Applause)


TEDを使った聞き取りL10

✤ Laurie Santos: Monkey economy as irrational as ours の前半✤ 今日の課題の長さ: 11分

✤ 41まで✤ 全体は19分30秒ほど

✤穴埋め方式✤ 長い目のユニットごとに2回反復


Documents

English Acquisition IAk, IIA fclsl.hi.h.kyoto-u.ac.jp/.../KIT-11A/L10-slides-EA1Ak2Af.pdf2. 正解の解説 休憩5分 後半40分 • TEDを使った聴き取り訓練の2回目 (L10)

English Acquisition IAk, IIA fclsl.hi.h.kyoto-u.ac.jp/.../KIT-11A/L10-slides-EA1Ak2Af.pdf2. 正解の解説休憩5分後半40分 • TEDを使った聴き取り訓練の2回目 (L10)