L4 for English Acquisition I Bk and II Bi, 2011

2011-11-01 (火)

L4 for English Acquisition I B k and II Bi , 2011

このスライドは次の

から入手できます

http://clsl.hi.h.kyoto-u.ac.jp/~kkuroda/lectures/11B-KIT/KIT-2011B-L03- slides.pdf

黒田 航

非常勤

連絡

✤

休講のお知らせ

✤ 2012年1月10日(火)は休講

✤ 2012月1月9日から13日まで松江で開催される Global WordNet Associationに 参加

✤ 1月31日が最終日=ボーナス試験 (L14に相当)

✤

欠席の扱い

✤ 欠席は3回まで，4回以上の欠席は無条件落第(らしい)

✤ けど，成績が十分なら出席は問題視しません

講義資料

✤

聴き取り用の教材は次の

ページから入手可能

✤ http://clsl.hi.h.kyoto-u.ac.jp/~kkuroda/lectures/KIT-11B.html

✤

授業時間外での予習や復習に利用して下さい

✤

速読に関して完全に同じことはできませんが，工

夫します

本日の予定

✤

前半

分

休憩

分を含む

✤ L3

の結果の報告

✤ L3

の正解の解説

✤

後半

分

✤ TED

を使った聴き取り訓練

✤ Cynthia Breazeal: The Rise of Personal Robots (14分30秒)を通して視聴

✤ 前半10分の聴き取り

Date

L3 の成績

採点法

✤

点数

✤ 完全正解 1.0 (◯で表示) と 不完全解 0.5 (△で表示)

✤

評価基準

✤ 素得点 S = ◯の数 + (△の数)/2

✤ 正答率 P = ◯の数/S

✤ 成績評価用の得点: S* = 100 × S/問題の総数 (e.g., 30)

✤

採点誤りがあるかも知れません

✤ 数え間違いや足り算間違をしますので，該当者は報告して下さい

出題への評価

Q1:

問題の数量

問題の数量

問題の数量

Q1:

問題の数量

問題の難しさ 問題の難しさ 問題の難しさ 問題の難しさ

Av. Stdev Max Min Av. Stdev Max Min

1Bk 3.35 0.55 4 2 2.16 0.82 4 1

2Bi 3.00 0.47 4 2 2.00 0.67 3 1

お願い: アンケートは表に書いて下さい

L3 の得点分布 1Bk と 2B i

✤

参加者

人

✤

平均点

標準偏差

✤

最高点

最低点

✤

得点グループ数

L3 の得点分布 1Bk

✤

受講者数

人

✤ 平均点: 12.55/n [62.73] 点

✤ 標準偏差: 2.32/n [11.58] 点

✤ 最高点: 19.00/n [95.00] 点

✤ 最低点: 4.00/n [37.50] 点

✤ n = 20

✤

得点グループ数

L3 の得点分布 2Bi

✤

受講者数

人

✤ 平均点: 12.00/n [60.00] 点

✤ 標準偏差: 2.03/n [10.14] 点

✤ 最高点: 15.00/n [75.00] 点

✤ 最低点: 9.00/n [45.00] 点

✤ n = 20

✤

得点グループ数

L3 の正解率分布 1B k と 2B i

✤

参加者

人

✤

平均

標準偏差

✤

最高

最低

✤

正答率のグループ数

L3 の正答率分布 1B k

✤

参加者

人

✤

平均

標準偏差

✤

最高

最低

✤

正答率のグループ

✤ 0.75

が中心のグループ

L3 の正答率分布 2B i

✤

参加者

人

✤

平均

標準偏差

✤

最高

最低

✤

正答率のグループ

✤

数が少ないので意味がない

平均得点の履歴

平均正解率の履歴

L3 の正解

誤りの傾向

✤ 1. fascinated ⇒ fasinated, fast, first, passed

✤ 2. enrich ⇒ (in) which

✤ 3. Mars ⇒ month, March, mouth, mouse, math, marks, most

✤ 4. robots ⇒ happy, about

✤ 5. interacting ⇒ interact, interacted, interesting

✤ 6. assume ⇒ soon

✤ 7. with ⇒ without, which

✤ 8. been

✤ 9. developed ⇒ about, build, do

✤ 10. introducing ⇒ NULL

✤ 11. find ⇒ fine

✤ 12. scary ⇒ scareley

✤ 13. cookies ⇒ cookie

✤ 14. learned ⇒ learn

✤ 15. push

✤ 16. otherwise ⇒ wise, words, ways, advice

✤ 17. using ⇒ use

✤ 18. use ⇒ uses

✤ 19. understand ⇒ answer

✤ 20. matter ⇒ mother, murder, mutter

聞き取りの心得その 2

✤

例

✤ it is hoped that

の発音は

✤ [ɨɗɨz hoʊp ðə]

✤

母音前の有声化

✤ it is ⇒ [ɨɗɨz]

✤ look at the ⇒[lʊɡæðə]

✤

アメリカ英語の

の発音

✤ bottle ⇒ [bʌɔɗl]

✤ atoms = Adums ⇒ [æbɗəmz]

✤

子音の前の語末子音の脱落

✤ hoped ⇒ hope [hoʊp]

✤ that ⇒ tha [ðə]

✤ th

音の変化

✤ that ⇒ nat [næ(t)]

01/13

✤ Ever since I was a little girl seeing Star Wars for the first time, I’ve been [1. fascinated] by this idea of personal

robots. And as a little girl, I loved the idea of a robot that interacted with us much more like a helpful, trusted

sidekick— something that would delight us, [2. enrich]

our lives and help us save a galaxy or two. So I knew

robots like that didn't really exist, but I knew I wanted to build them.

02/13

✤ So, 20 years passed— I am now a graduate student at MIT studying artificial intelligence, the year is 1997, and NASA

has just landed the first robot on [3. Mars]. But robots are still not in our home, ironically. And I remember thinking about all the reasons why that was the case. But one really struck me. Robotics had really been about interacting with things, not with people— certainly not in a social way that would be natural for us and would uh really help people accept [4.

robots] into our daily lives. For me, that was the white space, that's what robots could not do yet. And so that year, I started to build this robot, Kismet, the world’s first social robot.

03/13

✤ So, three years later— a lot of programming, working

with other graduate students in the lab— Kismet was ready to start [5. interacting] with people.

✤ Scientist: I wanna show you something.

✤ Kismet: (Nonsense).

✤ Scientist: This is a watch that my girlfriend gave me.

✤ Kismet: (Nonsense).

✤ Scientist: Yeah, look, it’s got a little blue light in it too. I almost lost it this week.

04/13

✤ So Kismet interacted with people like kind of a non-

verbal child or pre-verbal child, which I [6. assume] was fitting because it was really the first of its kind. It didn’t speak language, but it didn’t matter.

✤ This little robot was somehow able to tap into something deeply social within us. And [7. with] that, the promise of an entirely new way we could interact with robots.

05/13

✤ So over the past several years I’ve [8. been] continuing to explore this interpersonal dimension of robots, now at the media lab with my own team of incredibly talented

students. And one of my favorite robots is Leonardo. We [9.

developed] Leonardo in collaboration with Stan Winston Studio. And so I wanna show you a special moment for me of Leo. Uh, this is Matt Berlin interacting with Leo, [10.

introducing] Leo to a new object. And because it’s new, Leo doesn’t really know what to make of it. But sort of like us, he can actually learn about it from watching Matt’s

reaction.

06/13

✤ Hello, Leo.

✤ Leo, this is Cookie Monster.

✤ Can you [11. find] Cookie Monster?

✤ Leo, Cookie Monster is very bad. He’s very bad, Leo.

✤ Cookie Monster is very, very bad.

✤ He’s a [12. scary] monster, wants to get your [13. cookies].

07/13

✤ Alright, so Leo and Cookie might have gotten off to a little bit of a rough start, but they get along great now.

✤ So what I’ve [14. learned] through building these systems is that robots are actually a really intriguing social technology.

Where it's actually their ability to [15. push] our social

buttons and to interact with us like a partner that is a core part of their functionality. And with that shift in thinking, we can now start to imagine new questions, new possibilities for robots that we might not have uh thought about [16.

otherwise].

08/13

✤ But what do I mean when I say “push our social

buttons?” Well, one of the things that we’ve learned is that, if we design these robots to communicate with us

[17. using] the same body language, the same sort of non- verbal cues that people use— like Nexi, our humanoid

robot is doing here— what we find is that people respond to robots a lot like they respond to people. People [18.

use] these cues to determine things like how persuasive someone is, how likable, how engaging, how trustworthy.

It turns out it's the same for robots.

09/13

✤ It’s turning out now that robots are actually becoming a really interesting new scientific tool to [19. understand]

human behavior. To answer questions like, how is it that, from a brief encounter, we’re able to make an estimate of how trustworthy another person is? Mimicry’s

believed to play a role, but how? Is it the mimicking of particular gestures that [20. matter]?

Date

聴き取り訓練 L4

Cynthia Breazeal: The Rise of Personal Robots

✤ TED

の講演

✤

約

分

秒

回目は

分

秒，

回目は

分

✤

講演者

✤ Cynthia Breazeal

アメリカ英語の女性の母語話者

✤ 2001

年に世界初の社交的ロボット

を開発

✤

テーマ

✤

社交的ロボット

の過去と未来