The technology of translation - 6 Minute English

147,084 views ใƒป 2022-06-09

BBC Learning English


ืื ื ืœื—ืฅ ืคืขืžื™ื™ื ืขืœ ื”ื›ืชื•ื‘ื™ื•ืช ื‘ืื ื’ืœื™ืช ืœืžื˜ื” ื›ื“ื™ ืœื”ืคืขื™ืœ ืืช ื”ืกืจื˜ื•ืŸ. ื›ืชื•ื‘ื™ื•ืช ืžืชื•ืจื’ืžื•ืช ืžืชื•ืจื’ืžื•ืช ื‘ืžื›ื•ื ื”.

00:06
Hello. This is 6 Minute English from BBC Learning English. Iโ€™m Rob. ย 
0
6960
4400
ืฉืœื•ื. ื–ื•ื”ื™ 6 ื“ืงื•ืช ืื ื’ืœื™ืช ืžื‘ื™ืช BBC Learning English. ืื ื™ ืจื•ื‘.
00:11
And Iโ€™m Sam. Rob, Iโ€™m writing a letter to a friend in Spain ย 
1
11360
4000
ื•ืื ื™ ืกื. ืจื•ื‘, ืื ื™ ื›ื•ืชื‘ ืžื›ืชื‘ ืœื—ื‘ืจ ื‘ืกืคืจื“
00:15
and I need some help. Do you know the Spanish for, โ€˜itโ€™s rainingโ€™? ย 
2
15360
4320
ื•ืื ื™ ืฆืจื™ืš ืงืฆืช ืขื–ืจื”. ื”ืื ืืชื” ื™ื•ื“ืข ืืช ื”ืกืคืจื“ื™ืช ื‘ืฉื 'ื™ื•ืจื“ ื’ืฉื'?
00:19
Donโ€™t worry, I have this new app ... I just hold up my phone, scan the wordย 
3
19680
4400
ืืœ ืชื“ืื’, ื™ืฉ ืœื™ ืืช ื”ืืคืœื™ืงืฆื™ื” ื”ื—ื“ืฉื” ื”ื–ื•... ืื ื™ ืคืฉื•ื˜ ืžื—ื–ื™ืง ืืช ื”ื˜ืœืคื•ืŸ ืฉืœื™, ืกื•ืจืง ืืช ื”ืžื™ืœื”
00:24
I want translated, uh, 'esta lloviendoโ€™,
4
24080
3280
ืฉืื ื™ ืจื•ืฆื” ืฉืชื•ืจื’ื, ืื”, 'esta lloviendo',
00:27
is the Spanish for, โ€˜itโ€™s rainingโ€™. Amazing! In this programme weโ€™re discussing
5
27360
5200
ื–ื” ื”ืกืคืจื“ื™ ืฉืœ 'ื™ื•ืจื“ ื’ืฉื'. ืžื“ื”ื™ื! ื‘ืชื•ื›ื ื™ืช ื–ื• ืื ื• ื“ื ื™ื
00:32
language technologies โ€“ computers that can
6
32560
2800
ื‘ื˜ื›ื ื•ืœื•ื’ื™ื•ืช ืฉืคื” - ืžื—ืฉื‘ื™ื ืฉื™ื›ื•ืœื™ื
00:35
translate between languages. Modern software like Google Translate ย 
7
35360
4400
ืœืชืจื’ื ื‘ื™ืŸ ืฉืคื•ืช. ืชื•ื›ื ื” ืžื•ื“ืจื ื™ืช ื›ืžื• Google Translate
00:39
has transformed how we learn foreign languages, bringing us closer to a world ย 
8
39760
4720
ืฉื™ื ืชื” ืืช ื”ืื•ืคืŸ ืฉื‘ื• ืื ื—ื ื• ืœื•ืžื“ื™ื ืฉืคื•ืช ื–ืจื•ืช, ื•ืงื™ืจื‘ื” ืื•ืชื ื• ืœืขื•ืœื
00:44
where language is no longer a barrier to communication. ย 
9
44480
3520
ืฉื‘ื• ื”ืฉืคื” ื›ื‘ืจ ืœื ืžื”ื•ื•ื” ืžื—ืกื•ื ืœืชืงืฉื•ืจืช.
00:48
But how well do these computers knowย  what we really mean to say? ย 
10
48000
3760
ืื‘ืœ ืขื“ ื›ืžื” ื”ืžื—ืฉื‘ื™ื ื”ืืœื” ื™ื•ื“ืขื™ื ืžื” ืื ื—ื ื• ื‘ืืžืช ืžืชื›ื•ื•ื ื™ื ืœื•ืžืจ?
00:52
Later weโ€™ll find out exactly what machines can and canโ€™t translate, ย 
11
52560
4720
ืžืื•ื—ืจ ื™ื•ืชืจ ื ื’ืœื” ื‘ื“ื™ื•ืง ืื™ืœื• ืžื›ื•ื ื•ืช ื™ื›ื•ืœื•ืช ืœืชืจื’ื ื•ืžื” ืœื,
00:57
and, as usual, weโ€™ll be learning some new vocabulary as well. ย 
12
57280
2880
ื•ื›ืจื’ื™ืœ, ื ืœืžื“ ื’ื ืื•ืฆืจ ืžื™ืœื™ื ื—ื“ืฉ.
01:00
But first I have a question for you, Sam. The translation app I used just now isย 
13
60800
4800
ืื‘ืœ ืงื•ื“ื ื™ืฉ ืœื™ ืฉืืœื” ืืœื™ืš, ืกื. ืืคืœื™ืงืฆื™ื™ืช ื”ืชืจื’ื•ื ืฉื”ืฉืชืžืฉืชื™ ื‘ื” ื–ื” ืขืชื” ื”ื™ื
01:05
very recent, but thereโ€™s a
14
65600
1440
ืขื“ื›ื ื™ืช ืžืื•ื“, ืื‘ืœ ื™ืฉ
01:07
long history of computer mistranslations - times when computers got it badly wrong. ย 
15
67040
5760
ื”ื™ืกื˜ื•ืจื™ื” ืืจื•ื›ื” ืฉืœ ืชืจื’ื•ื ืฉื’ื•ื™ ื‘ืžื—ืฉื‘ - ื–ืžื ื™ื ืฉื‘ื”ื ืžื—ืฉื‘ื™ื ื˜ืขื• ื‘ืฆื•ืจื” ื—ืžื•ืจื”.
01:13
In 1987, the American airline, Braniff, ran television adverts promoting ย 
16
73360
5280
ื‘ืฉื ืช 1987, ื—ื‘ืจืช ื”ืชืขื•ืคื” ื”ืืžืจื™ืงืื™ืช, ื‘ืจื ื™ืฃ, ื”ืคืขื™ืœื” ืคืจืกื•ืžื•ืช ื‘ื˜ืœื•ื•ื™ื–ื™ื” ื”ืžืงื“ืžื•ืช
01:18
the all-leather seats installed on their flights to Mexico. ย 
17
78640
4080
ืืช ืžื•ืฉื‘ื™ ื”ืขื•ืจ ื”ืžื•ืชืงื ื™ื ื‘ื˜ื™ืกื•ืชื™ื” ืœืžืงืกื™ืงื•.
01:22
But how was its โ€œfly in leatherโ€ advertising slogan mistranslated into Spanish? ย 
18
82720
5680
ืื‘ืœ ืื™ืš ืชื•ืจื’ื ืœื ื ื›ื•ืŸ ืืช ืกื™ืกืžืช ื”ืคืจืกื•ื "ืœืขื•ืฃ ื‘ืขื•ืจ" ืœืกืคืจื“ื™ืช?
01:28
Did the advert say: a) fly in lava ย 
19
88400
3840
ื”ืื ื‘ืžื•ื“ืขื” ื ืืžืจ: ื) ืœืขื•ืฃ ื‘ืœื‘ื”
01:32
b) fly on a cow c) fly naked ย 
20
92240
3360
ื‘) ืœืขื•ืฃ ืขืœ ืคืจื” ื’) ืœืขื•ืฃ ืขื™ืจื•ื
01:35
Hmm, I have a feeling it might be, c) fly naked. ย 
21
95600
4240
ื”ืžืž, ื™ืฉ ืœื™ ื”ืจื’ืฉื” ืฉื–ื” ื™ื›ื•ืœ ืœื”ื™ื•ืช, ื’) ืœืขื•ืฃ ืขื™ืจื•ื.
01:40
Ok, Sam. Iโ€™ll reveal the correct answer later in the programme. ย 
22
100480
3680
ื‘ืกื“ืจ, ืกื. ืื ื™ ืื’ืœื” ืืช ื”ืชืฉื•ื‘ื” ื”ื ื›ื•ื ื” ื‘ื”ืžืฉืš ื”ืชื•ื›ื ื™ืช.
01:44
Computer software used to rely on rules-based translation, ย 
23
104800
4080
ืชื•ื›ื ืช ืžื—ืฉื‘ ื”ืžืฉืžืฉืช ืœื”ืกืชืžืš ืขืœ ืชืจื’ื•ื ืžื‘ื•ืกืก ื›ืœืœื™ื,
01:48
applying the grammar rules of one language to another. ย 
24
108880
3040
ื”ื—ืœื” ืืช ื›ืœืœื™ ื”ื“ืงื“ื•ืง ืฉืœ ืฉืคื” ืื—ืช ืขืœ ืื—ืจืช.
01:51
That worked fine for simple words and phrases but what happens when a translator ย 
25
111920
4960
ื–ื” ืขื‘ื“ ืžืฆื•ื™ืŸ ืขื‘ื•ืจ ืžื™ืœื™ื ื•ื‘ื™ื˜ื•ื™ื™ื ืคืฉื•ื˜ื™ื, ืื‘ืœ ืžื” ืงื•ืจื” ื›ืืฉืจ ืžืชืจื’ื
01:56
comes across more complex language for example metaphors ย 
26
116880
3520
ื ืชืงืœ ื‘ืฉืคื” ืžื•ืจื›ื‘ืช ื™ื•ืชืจ, ืœืžืฉืœ ืžื˜ืคื•ืจื•ืช
02:00
- expressions used to describe one thing by comparing it to another. ย 
27
120400
4400
- ื‘ื™ื˜ื•ื™ื™ื ื”ืžืฉืžืฉื™ื ืœืชื™ืื•ืจ ื“ื‘ืจ ืื—ื“ ืขืœ ื™ื“ื™ ื”ืฉื•ื•ืืชื• ืœืื—ืจ.
02:05
Lane Greene is a language journalist and the author of the book,
28
125360
4240
ืœื™ื™ืŸ ื’ืจื™ืŸ ื”ื™ื ืขื™ืชื•ื ืื™ืช ืœืฉื•ืŸ ื•ืžื—ื‘ืจืช ื”ืกืคืจ,
02:09
Talk on the Wild Side. Here he explains to BBC
29
129600
3760
Talk on the Wild Side. ื›ืืŸ ื”ื•ื ืžืกื‘ื™ืจ ืœืชื•ื›ื ื™ืช BBC
02:13
Radio 4 programme, Word of Mouth, how apps like Google Translate ย 
30
133360
4880
Radio 4, ืžืคื” ืœืื•ื–ืŸ, ื›ื™ืฆื“ ืืคืœื™ืงืฆื™ื•ืช ื›ืžื• Google Translate
02:18
allow users to manually translate metaphors: If I say, โ€˜itโ€™s raining cats and dogsโ€™ ย 
31
138240
6480
ืžืืคืฉืจื•ืช ืœืžืฉืชืžืฉื™ื ืœืชืจื’ื ืžื˜ืคื•ืจื•ืช ื‘ืื•ืคืŸ ื™ื“ื ื™: ืื ืื ื™ ืื•ืžืจ, 'ื™ื•ืจื“ ื’ืฉื ืขืœ ื—ืชื•ืœื™ื ื•ื›ืœื‘ื™ื'
02:25
and it literally translates, โ€˜esta lloviendo perros y gatosโ€™ ย 
32
145840
3360
ื•ื–ื” ืžืชืจื’ื, ืคืฉื•ื˜ื• ื›ืžืฉืžืขื•, ' esta lloviendo perros y gatos'
02:29
in Spanish, that wonโ€™t make any sense, but I think somebody at Google ย 
33
149200
2800
ื‘ืกืคืจื“ื™ืช , ื–ื” ืœื ื™ื”ื™ื” ื”ื’ื™ื•ื ื™, ืื‘ืœ ืื ื™ ื—ื•ืฉื‘ ืฉืžื™ืฉื”ื• ื‘-Google
02:32
will have inputted the phrase, โ€˜lueve a cรกntarosโ€™ which is the phrase, ย 
34
152000
5360
ื”ื–ื™ืŸ ืืช ื”ื‘ื™ื˜ื•ื™, ' lueve a cรกntaros' ืฉื”ื•ื ื”ื‘ื™ื˜ื•ื™, ย  '
02:37
โ€˜itโ€™s raining pitchersโ€™, or โ€˜itโ€™s raining jugs of waterโ€™, ย 
35
157360
2880
ื™ื•ืจื“ ื›ื“ื™ื', ืื• 'ื™ื•ืจื“ ื’ืฉื ืขืœ ืงื ืงื ื™ ืžื™ื',
02:40
so that the whole chunk, โ€˜raining cats and dogsโ€™, ย 
36
160240
3120
ื›ืš ืฉื›ื•ืœื• chunk, ' ืžื˜ืืคื•ืจื” ืฉืœ ื—ืชื•ืœื™ื ื•ื›ืœื‘ื™ื',
02:43
is translated into the equivalent metaphor in Spanish. ย 
37
163360
4080
ืžืชื•ืจื’ื ืœืžื˜ืืคื•ืจื” ื”ืžืงื‘ื™ืœื” ื‘ืกืคืจื“ื™ืช.
02:48
Lane wants to translate the phrase, ย 
38
168160
2000
ืœื™ื™ืŸ ืจื•ืฆื” ืœืชืจื’ื ืืช ื”ื‘ื™ื˜ื•ื™,
02:50
itโ€™s raining cats and dogs, something that people sometimes say ย 
39
170160
3920
ื™ื•ืจื“ ื’ืฉื ืฉืœ ื—ืชื•ืœื™ื ื•ื›ืœื‘ื™ื, ืžืฉื”ื• ืฉืื ืฉื™ื ืื•ืžืจื™ื ืœืคืขืžื™ื
02:54
when itโ€™s raining heavily. It wouldnโ€™t make sense to translate this ย 
40
174080
3680
ื›ืฉื™ื•ืจื“ ื’ืฉื ื—ื–ืง. ื–ื” ืœื ื”ื’ื™ื•ื ื™ ืœืชืจื’ื ืืช
02:57
phrase into another language literally, word by word. One solution is to translate ย 
41
177760
5840
ื”ื‘ื™ื˜ื•ื™ ื”ื–ื” ืœืฉืคื” ืื—ืจืช, ืžื™ืœื•ืœื™ืช, ืžื™ืœื” ืื—ืจ ืžื™ืœื”. ืคืชืจื•ืŸ ืื—ื“ ื”ื•ื ืœืชืจื’ื
03:03
the whole idiom as a chunk, or a large part of text or language. ย 
42
183600
5040
ืืช ื›ืœ ื”ื‘ื™ื˜ื•ื™ ื›ื—ืœืง, ืื• ื—ืœืง ื’ื“ื•ืœ ืžื”ื˜ืงืกื˜ ืื• ื”ืฉืคื”.
03:09
This works for phrases and idioms that people regularly use in the same way ย 
43
189440
4720
ื–ื” ืขื•ื‘ื“ ืขื‘ื•ืจ ื‘ื™ื˜ื•ื™ื™ื ื•ื ื™ื‘ื™ื ืฉืื ืฉื™ื ืžืฉืชืžืฉื™ื ื‘ื”ื ื‘ืื•ืคืŸ ืงื‘ื•ืข ื‘ืื•ืชื• ืื•ืคืŸ
03:14
because they can be taught to a computer. But what happens when someone like a poet ย 
44
194160
5040
ื›ื™ ืืคืฉืจ ืœืœืžื“ ืื•ืชื ืœืžื—ืฉื‘. ืื‘ืœ ืžื” ืงื•ืจื” ื›ืฉืžื™ืฉื”ื• ื›ืžื• ืžืฉื•ืจืจ
03:19
writes a completely new sentence which has never been written before? ย 
45
199200
3360
ื›ื•ืชื‘ ืžืฉืคื˜ ื—ื“ืฉ ืœื’ืžืจื™ ืฉืžืขื•ืœื ืœื ื ื›ืชื‘ ืงื•ื“ื ืœื›ืŸ?
03:23
Lane Greene thinks that even the smartest software ย 
46
203200
2800
ืœื™ื™ืŸ ื’ืจื™ืŸ ื—ื•ืฉื‘ ืฉืืคื™ืœื• ื”ืชื•ื›ื ื” ื”ื—ื›ืžื” ื‘ื™ื•ืชืจ
03:26
couldnโ€™t deal with that, as he told Michael Rosen, ย 
47
206000
2800
ืœื ื™ื›ืœื” ืœื”ืชืžื•ื“ื“ ืขื ื–ื”, ื›ืคื™ ืฉืืžืจ ืœืžื™ื™ืงืœ ืจื•ื–ืŸ,
03:28
poet and presenter of BBC Radio 4โ€™s Word of Mouth: ย 
48
208800
3440
ืžืฉื•ืจืจ ื•ืžื’ื™ืฉ ื”-BBC Radio 4's Word of Mouth:
03:33
โ€ฆif a poet writes a new one then the machine is not going to pick it up, ย 
49
213280
3840
...ืื ืžืฉื•ืจืจ ื›ื•ืชื‘ ืื—ื“ ื—ื“ืฉ ืื– ื”ืžื›ื•ื ื” ืœื ืžืชื›ื•ื•ื ืช ืœืงืœื•ื˜ ืืช ื–ื” ,
03:37
and itโ€™s going to have a struggle, isnโ€™t it? Sorry, Iโ€™m sticking up for poetry here ย 
50
217120
3920
ื•ื™ื”ื™ื” ืœื–ื” ืžืื‘ืง, ืœื? ืกืœื™ื—ื”, ืื ื™ ื“ื•ื’ืœ ื‘ืฉื™ืจื” ื›ืืŸ
03:41
and trying to claim that itโ€™s untranslatable โ€“ can you hear what Iโ€™m doing? ย 
51
221040
3200
ื•ืžื ืกื” ืœื˜ืขื•ืŸ ืฉื”ื™ื ื‘ืœืชื™ ื ื™ืชื ืช ืœืชืจื’ื•ื - ื”ืื ืืชื” ืฉื•ืžืข ืžื” ืื ื™ ืขื•ืฉื”?
03:44
I hear you, and in a war against the machines, our advantage is novelty and creativity. ย 
52
224240
5920
ืื ื™ ืฉื•ืžืข ืื•ืชืš, ื•ื‘ืžืœื—ืžื” ื ื’ื“ ื”ืžื›ื•ื ื•ืช, ื”ื™ืชืจื•ืŸ ืฉืœื ื• ื”ื•ื ื—ื™ื“ื•ืฉ ื•ื™ืฆื™ืจืชื™ื•ืช.
03:50
So youโ€™re right that machines will be great at anything that is rote, ย 
53
230160
2880
ืื– ืืชื” ืฆื•ื“ืง ืฉืžื›ื•ื ื•ืช ื™ื”ื™ื• ืžืขื•ืœื•ืช ื‘ื›ืœ ื“ื‘ืจ ืฉื”ื•ื ืžื™ื•ืฉืŸ,
03:53
anything thatโ€™s already been done a million times can be automated. ย 
54
233040
3600
ื›ืœ ื“ื‘ืจ ืฉื›ื‘ืจ ื ืขืฉื” ืžื™ืœื™ื•ืŸ ืคืขืžื™ื ื™ื›ื•ืœ ืœื”ื™ื•ืช ืื•ื˜ื•ืžื˜ื™.
03:56
So you and I with our pre-frontal cortexes can try to come up with phrases ย 
55
236640
5840
ืื– ืืชื” ื•ืื ื™ ืขื ื”ืงื•ืจื˜ืงืกื™ื ื”ืคืจื”-ืคืจื•ื ื˜ืืœื™ื™ื ืฉืœื ื• ื™ื›ื•ืœื™ื ืœื ืกื•ืช ืœื”ืžืฆื™ื ื‘ื™ื˜ื•ื™ื™ื
04:02
thatโ€™ll flummox the computer and so keep our jobs. ย 
56
242480
3440
ืฉื™ืžืจื—ื• ืืช ื”ืžื—ืฉื‘ ื•ื›ืš ื™ืฉืžืจื• ืขืœ ื”ืขื‘ื•ื“ื” ืฉืœื ื•.
04:05
When we say machines โ€œlearnโ€ a language, we really mean they have been trained ย 
57
245920
5120
ื›ืฉืื ื—ื ื• ืื•ืžืจื™ื ืฉืžื›ื•ื ื•ืช "ืœื•ืžื“ื™ื" ืฉืคื”, ืื ื—ื ื• ื‘ืืžืช ืžืชื›ื•ื•ื ื™ื ืฉื”ื ืื•ืžื ื•
04:11
to identify patterns in millions and millions of translations. ย 
58
251040
3920
ืœื–ื”ื•ืช ื“ืคื•ืกื™ื ื‘ืžื™ืœื™ื•ื ื™ ื•ืžื™ืœื™ื•ื ื™ ืชืจื’ื•ืžื™ื.
04:15
Computers can only learn by rote - by memory in order to repeat information ย 
59
255680
5920
ืžื—ืฉื‘ื™ื ื™ื›ื•ืœื™ื ืœืœืžื•ื“ ืจืง ืขืœ ื™ื“ื™ ื–ื™ื›ืจื•ืŸ - ืขืœ ืžื ืช ืœื—ื–ื•ืจ ืขืœ ืžื™ื“ืข
04:21
rather than to properly understand it. This kind of rote learning ย 
60
261600
4480
ื‘ืžืงื•ื ืœื”ื‘ื™ืŸ ืื•ืชื• ื›ืจืื•ื™. ืœืžื™ื“ื” ืžื”ืกื•ื’ ื”ื–ื”
04:26
can be easily automated - done by machines instead of humans. ย 
61
266080
4240
ื™ื›ื•ืœื” ืœื”ื™ื•ืช ืื•ื˜ื•ืžื˜ื™ืช ื‘ืงืœื•ืช - ื ืขืฉื” ืขืœ ื™ื“ื™ ืžื›ื•ื ื•ืช ื‘ืžืงื•ื ื‘ื ื™ ืื“ื.
04:30
But itโ€™s completely different from human learning requiring creative thinking ย 
62
270320
4240
ืื‘ืœ ื–ื” ืฉื•ื ื” ืœื—ืœื•ื˜ื™ืŸ ืžืœืžื™ื“ื” ืื ื•ืฉื™ืช ื”ื“ื•ืจืฉืช ื—ืฉื™ื‘ื” ื™ืฆื™ืจืชื™ืช
04:34
which would flummox โ€“ or confuse, even the most sophisticated machine. ย 
63
274560
4240
ืฉื”ื™ื™ืชื” ืžื‘ื•ืœื‘ืœืช - ืื• ืชื‘ืœื‘ืœ, ืืคื™ืœื• ืืช ื”ืžื›ื•ื ื” ื”ืžืชื•ื—ื›ืžืช ื‘ื™ื•ืชืจ.
04:39
Bad news for translation software, but good news for humans ย 
64
279600
3520
ื—ื“ืฉื•ืช ืจืขื•ืช ืœืชื•ื›ื ืช ืชืจื’ื•ื, ืื‘ืœ ื—ื“ืฉื•ืช ื˜ื•ื‘ื•ืช ืœื‘ื ื™ ืื“ื
04:43
who use different languages in their jobs โ€“ like us! ย 
65
283120
3040
ืฉืžืฉืชืžืฉื™ื ื‘ืฉืคื•ืช ืฉื•ื ื•ืช ื‘ืขื‘ื•ื“ืชื - ื›ืžื•ื ื•!
04:47
Yes, if only Braniff Airlines had relied on human translators, ย 
66
287280
3360
ื›ืŸ, ืื ืจืง ื‘ืจื ื™ืฃ ืื™ื™ืจืœื™ื™ื ืก ื”ื™ื™ืชื” ืžืกืชืžื›ืช ืขืœ ืžืชืจื’ืžื™ื ืื ื•ืฉื™ื™ื,
04:50
they might have avoided an embarrassing situation. ย 
67
290640
2880
ืื•ืœื™ ื”ื ื”ื™ื• ื ืžื ืขื™ื ืžืžืฆื‘ ืžื‘ื™ืš.
04:53
Ah, in your question you asked how Braniffโ€™s television advertisement ย 
68
293520
4480
ืื”, ื‘ืฉืืœืชืš ืฉืืœืช ืื™ืš ืคืจืกื•ืžืช ื”ื˜ืœื•ื•ื™ื–ื™ื” ืฉืœ ื‘ืจื ื™ืฃ
04:58
โ€œfly in leatherโ€ was translated into Spanish. I guessed it was mistranslated as โ€œfly nakedโ€™. ย 
69
298000
7920
"ืœืขื•ืฃ ื‘ืขื•ืจ" ืชื•ืจื’ืžื” ืœืกืคืจื“ื™ืช. ื ื™ื—ืฉืชื™ ืฉื–ื” ืœื ืชื•ืจื’ื ื›"ืœืขื•ืฃ ืขื™ืจื•ื".
05:05
Which wasโ€ฆ the correct answer! Braniff translated its "fly in leather" slogan ย 
70
305920
5440
ืžื” ืฉื”ื™ื™ืชื”... ื”ืชืฉื•ื‘ื” ื”ื ื›ื•ื ื”! ื‘ืจื ื™ืฃ ืชืจื’ืžื” ืืช ื”ืกืœื•ื’ืŸ "ืœืขื•ืฃ ื‘ืขื•ืจ" ืฉืœื•
05:11
as fly "en cuero," which sounds like
71
311360
2960
ื›-fly "en cuero", ืฉื ืฉืžืข ื›ืžื•
05:14
Spanish slang for "fly nakedโ€. OK, letโ€™s recap the vocabulary ย 
72
314320
4880
ืกืœื ื’ ืกืคืจื“ื™ ืœ"ืœืขื•ืฃ ืขื™ืจื•ื". ื‘ืกื“ืจ, ื‘ื•ืื• ื ืกื›ื ืืช ืื•ืฆืจ ื”ืžื™ืœื™ื
05:19
from this programme about language translations ย 
73
319200
2880
ืžื”ืชื•ื›ื ื™ืช ื”ื–ื• ืขืœ ืชืจื’ื•ืžื™ ืฉืคื•ืช
05:22
which are automated - done by machines instead of humans. ย 
74
322080
3840
ืฉื”ื ืื•ื˜ื•ืžื˜ื™ื™ื - ืฉื ืขืฉื• ืขืœ ื™ื“ื™ ืžื›ื•ื ื•ืช ื‘ืžืงื•ื ื‘ื ื™ ืื“ื.
05:26
Often found in poetry, a metaphor is a way of describing ย 
75
326960
3760
ืœืขืชื™ื ืงืจื•ื‘ื•ืช ื ืžืฆื ื‘ืฉื™ืจื”, ืžื˜ืคื•ืจื” ื”ื™ื ื“ืจืš ืœืชืืจ
05:30
something by reference to something else. When itโ€™s raining heavily ย 
76
330720
4160
ืžืฉื”ื• ื‘ื”ืชื™ื™ื—ืกื•ืช ืœืžืฉื”ื• ืื—ืจ. ื›ืืฉืจ ื™ื•ืจื“ ื’ืฉื ื—ื–ืง
05:34
you might use the idiom, itโ€™s raining cats and dogs! ย 
77
334880
2960
ืืชื” ื™ื›ื•ืœ ืœื”ืฉืชืžืฉ ื‘ื‘ื™ื˜ื•ื™, ื–ื” ื™ื•ืจื“ ื’ืฉื ืขืœ ื—ืชื•ืœื™ื ื•ื›ืœื‘ื™ื!
05:38
A chunk is a large part of something. Rote learning involves memorising information ย 
78
338560
6080
ื ืชื— ื”ื•ื ื—ืœืง ื’ื“ื•ืœ ืžืžืฉื”ื•. ืœื™ืžื•ื“ ืจื•ื˜ื™ื ื” ื›ื•ืœืœ ืฉื™ื ื•ืŸ ืžื™ื“ืข
05:44
which you repeat but donโ€™t really understand. And finally, if someone is flummoxed, ย 
79
344640
6000
ืืฉืจ ืืชื” ื—ื•ื–ืจ ืื‘ืœ ืœื ืžืžืฉ ืžื‘ื™ืŸ. ื•ืœื‘ืกื•ืฃ, ืื ืžื™ืฉื”ื• ืžื‘ื•ืœื‘ืœ,
05:50
theyโ€™re so confused that they donโ€™t know what to do! ย 
80
350640
2480
ื”ื•ื ื›ืœ ื›ืš ืžื‘ื•ืœื‘ืœ ืฉื”ื•ื ืœื ื™ื•ื“ืข ืžื” ืœืขืฉื•ืช!
05:54
Once again our six minutes are up! Join us again soon for more trending topics ย 
81
354400
5040
ืฉื•ื‘ ืฉืฉ ื”ื“ืงื•ืช ืฉืœื ื• ื ื’ืžืจื•! ื”ืฆื˜ืจืฃ ืืœื™ื ื• ืฉื•ื‘ ื‘ืงืจื•ื‘ ืœื ื•ืฉืื™ื ื˜ืจื ื“ื™ื™ื ื ื•ืกืคื™ื
05:59
and useful vocabulary here at 6 Minute English. ย 
82
359440
3360
ื•ืฉื™ืžื•ืฉื™ื™ื ืื•ืฆืจ ืžื™ืœื™ื ื›ืืŸ ื‘-6 ื“ืงื•ืช ืื ื’ืœื™ืช.
06:02
Goodbye for now! Bye!
83
362800
3040
ืœื”ืชืจืื•ืช ืœืขืช ืขืชื”! ื‘ื™ื™!
ืขืœ ืืชืจ ื–ื”

ืืชืจ ื–ื” ื™ืฆื™ื’ ื‘ืคื ื™ื›ื ืกืจื˜ื•ื ื™ YouTube ื”ืžื•ืขื™ืœื™ื ืœืœื™ืžื•ื“ ืื ื’ืœื™ืช. ืชื•ื›ืœื• ืœืจืื•ืช ืฉื™ืขื•ืจื™ ืื ื’ืœื™ืช ื”ืžื•ืขื‘ืจื™ื ืขืœ ื™ื“ื™ ืžื•ืจื™ื ืžื”ืฉื•ืจื” ื”ืจืืฉื•ื ื” ืžืจื—ื‘ื™ ื”ืขื•ืœื. ืœื—ืฅ ืคืขืžื™ื™ื ืขืœ ื”ื›ืชื•ื‘ื™ื•ืช ื‘ืื ื’ืœื™ืช ื”ืžื•ืฆื’ื•ืช ื‘ื›ืœ ื“ืฃ ื•ื™ื“ืื• ื›ื“ื™ ืœื”ืคืขื™ืœ ืืช ื”ืกืจื˜ื•ืŸ ืžืฉื. ื”ื›ืชื•ื‘ื™ื•ืช ื’ื•ืœืœื•ืช ื‘ืกื ื›ืจื•ืŸ ืขื ื”ืคืขืœืช ื”ื•ื•ื™ื“ืื•. ืื ื™ืฉ ืœืš ื”ืขืจื•ืช ืื• ื‘ืงืฉื•ืช, ืื ื ืฆื•ืจ ืื™ืชื ื• ืงืฉืจ ื‘ืืžืฆืขื•ืช ื˜ื•ืคืก ื™ืฆื™ืจืช ืงืฉืจ ื–ื”.

https://forms.gle/WvT1wiN1qDtmnspy7