WEBVTT 0:00:00.000 --> 0:00:10.115 That easy to say this is a good translation and this is a bad translation. 0:00:10.115 --> 0:00:12.947 How can we evaluate? 0:00:13.413 --> 0:00:26.083 We will put an emphasis on machine translation because that is currently the state of the 0:00:26.083 --> 0:00:26.787 art. 0:00:28.028 --> 0:00:35.120 But we are now focused on the details of neural networks where we are describing the basic 0:00:35.120 --> 0:00:39.095 ideas and how to use the info machine translation. 0:00:39.095 --> 0:00:41.979 This is not a neural network course. 0:00:42.242 --> 0:00:49.574 If you have some background in Neo Networks, that is of course of an advantage, but it should 0:00:49.574 --> 0:00:51.134 not be a challenge. 0:00:51.134 --> 0:00:58.076 If you have not done the details, we'll shortly cover the background and the main ideas. 0:00:58.076 --> 0:01:00.338 How can we use them for for? 0:01:00.280 --> 0:01:06.880 Machine translation: We will starve the first two, three lectures with some like more traditional 0:01:06.880 --> 0:01:12.740 approaches how they work because they still give some good intuition, some good ideas. 0:01:12.872 --> 0:01:17.141 And they help us to understand where our systems might be better. 0:01:17.657 --> 0:01:22.942 And yeah, we have an innocence on really what do we need to do to build a strong system. 0:01:23.343 --> 0:01:35.534 And then we have a part on experience where it's about how to build the systems and how 0:01:35.534 --> 0:01:37.335 to apply it. 0:01:39.799 --> 0:01:47.774 For additional reading materials, so we have the slides on the website. 0:01:47.774 --> 0:01:55.305 There is also links to papers which cover the topic of the lecture. 0:01:55.235 --> 0:01:58.436 If You'd Like to Study Additional Books. 0:01:59.559 --> 0:02:07.158 Think the most relevant is this machine translation from Philip Kurnan, which gives an introduction 0:02:07.158 --> 0:02:09.210 about machine translation. 0:02:09.210 --> 0:02:15.897 But this lecture is, of course, not a one to one like we don't go through the book, but 0:02:15.897 --> 0:02:17.873 it covers related topics. 0:02:18.678 --> 0:02:25.094 Is a previous version of that statistical machine translation focusing on that part, 0:02:25.094 --> 0:02:28.717 and we cover some of that part rather than all. 0:02:28.717 --> 0:02:35.510 If you want to have more basics about natural language processing, this might be helpful. 0:02:39.099 --> 0:02:53.738 In addition, there is an online course on machine translation which we also develop here 0:02:53.738 --> 0:02:57.521 at which is available. 0:02:57.377 --> 0:03:04.894 Input where you're, of course, free to use that I might give you some other type of presentation 0:03:04.894 --> 0:03:07.141 of the lecture important is. 0:03:07.141 --> 0:03:14.193 It's, of course, a lot shorter and book doesn't cover all the topics which you're covering 0:03:14.193 --> 0:03:15.432 in the lecture. 0:03:15.655 --> 0:03:19.407 So, of course, for the exam everything which was in the lecture is important. 0:03:19.679 --> 0:03:25.012 This covers like the first half where don't know exactly the first X lectures. 0:03:26.026 --> 0:03:28.554 Feel free to have a look at that. 0:03:28.554 --> 0:03:29.596 It's shorter. 0:03:29.596 --> 0:03:36.438 Maybe there's some of you interesting to have very short videos or after the lecture single 0:03:36.438 --> 0:03:39.934 this topic I didn't understand want to repeat. 0:03:40.260 --> 0:03:50.504 Then this might be helpful, but it's important that there is more content in the lecture. 0:03:53.753 --> 0:04:02.859 The exam will be minutes and oral exam and just make an appointment and then. 0:04:05.305 --> 0:04:09.735 If you think this is a really cool topic, want to hear more. 0:04:09.735 --> 0:04:14.747 There's two similars, one on advanced topics in machine translation. 0:04:15.855 --> 0:04:24.347 Which is every Thursday and there is one which was already on Monday. 0:04:24.347 --> 0:04:34.295 But if you're interested in speech translation to contact us and there, I think,. 0:04:34.734 --> 0:04:47.066 Then there are other lectures, one more learning by Professor Vival, and for us some of you 0:04:47.066 --> 0:04:48.942 have already. 0:04:48.888 --> 0:04:55.496 Lecture, which is related but of discovering more general natural language processing than 0:04:55.496 --> 0:04:57.530 will be again available in. 0:04:57.597 --> 0:05:07.108 Winter semester, and then we are concentrating on the task of machine translation and mighty. 0:05:11.191 --> 0:05:14.630 Yeah, and also there's an automatic speech emission problem. 0:05:16.616 --> 0:05:27.150 And this is a bit what we are planning to talk about in this semester. 0:05:27.150 --> 0:05:30.859 Today we have a general. 0:05:31.371 --> 0:05:37.362 Then on Thursday we are doing a bit of a different lecture and that's about the linguistic. 0:05:37.717 --> 0:05:42.475 It may be quite different from what you're more computer scientist, what you've done there, 0:05:42.475 --> 0:05:43.354 but don't worry. 0:05:43.763 --> 0:05:49.051 We're coming in a very basic thing that I think it's important if you're dealing with 0:05:49.051 --> 0:05:53.663 natural language to have a bit of an understanding of what language isn't. 0:05:53.663 --> 0:05:59.320 Maybe I've learned about that in high school, but also for you this I guess some years ago. 0:05:59.619 --> 0:06:07.381 And so it's a bit of yeah, it better understand also what other challenges there. 0:06:07.307 --> 0:06:16.866 And especially since we are all dealing with our mother time, it may be English, but there 0:06:16.866 --> 0:06:25.270 is a lot of interesting phenomena which would not occur in these two languages. 0:06:25.625 --> 0:06:30.663 And therefore we'll also look a bit into what are things which might happen in other languages. 0:06:30.930 --> 0:06:35.907 If we want to build machine translation, of course we want to build machine Translation 0:06:35.907 --> 0:06:36.472 for many. 0:06:38.178 --> 0:06:46.989 Then we will see a lot of these machine learning based how to get the data and process the data 0:06:46.989 --> 0:06:47.999 next week. 0:06:48.208 --> 0:07:03.500 And then we'll have one lecture about statistical machine translation, which was the approach 0:07:03.500 --> 0:07:06.428 for twenty years. 0:07:07.487 --> 0:07:17.308 And then maybe surprisingly very early we'll talk about evaluation and this is because evaluation 0:07:17.308 --> 0:07:24.424 is really essential for machine translation and it's very challenging. 0:07:24.804 --> 0:07:28.840 To decide if machine translation output is good or bad is really challenging. 0:07:29.349 --> 0:07:38.563 If you see another translation for a machine to decide is not as difficult and even for 0:07:38.563 --> 0:07:48.387 a machine translation output and ask them to rate, you'll get three different answers: And 0:07:48.387 --> 0:07:55.158 so it's worse to investigate it, and of course it's also important to have that at the beginning 0:07:55.158 --> 0:08:01.928 because if we're later talking about some techniques, it will be always saying this technique is 0:08:01.928 --> 0:08:03.813 better by x percent or so. 0:08:04.284 --> 0:08:06.283 And we'll also have a practical good course of this. 0:08:06.746 --> 0:08:16.553 Then we're going to build language models which are in point to translation models. 0:08:16.736 --> 0:08:28.729 After the half you have a basic understanding of what and basic machine translation. 0:08:29.029 --> 0:08:39.065 And then on the second part of the lecture we will cover more advanced topics. 0:08:39.065 --> 0:08:42.369 What are the challenging? 0:08:43.463 --> 0:08:48.035 One challenge is, of course, about additional resources about data. 0:08:48.208 --> 0:08:53.807 So the question is how can we get more data or better data and their different ways of 0:08:53.807 --> 0:08:54.258 doing? 0:08:54.214 --> 0:09:00.230 Our thralling data will look into our building systems which not translate between one language 0:09:00.230 --> 0:09:06.122 but which translate between fifteen languages and youth knowledge and share knowledge between 0:09:06.122 --> 0:09:09.632 the language so that for each pair they need less data. 0:09:11.751 --> 0:09:19.194 And then we'll have something about efficiency. 0:09:19.194 --> 0:09:27.722 That is, of course, with more and more complex models. 0:09:27.647 --> 0:09:33.053 Because then nobody can afford to do that, so how can you build really efficient things? 0:09:33.393 --> 0:09:38.513 Who also like energy is getting more expensive so it's even more important to build systems. 0:09:39.419 --> 0:09:43.447 We're Looking to Biases So. 0:09:43.423 --> 0:09:50.364 That is a machine translation quite interesting because some information are represented different 0:09:50.364 --> 0:09:51.345 in languages. 0:09:51.345 --> 0:09:55.552 So if you think about German, there is always clear or not. 0:09:55.552 --> 0:10:00.950 But in a lot of situations, it's clear if you talk about to teach her about. 0:10:01.321 --> 0:10:03.807 Another Person If It's Male or Female. 0:10:04.204 --> 0:10:13.832 From English to German you don't have this information, so how do you generate that and 0:10:13.832 --> 0:10:15.364 what systems? 0:10:15.515 --> 0:10:24.126 Will just assume things and we'll see that exactly this is happening, so in order to address 0:10:24.126 --> 0:10:27.459 these challenges and try to reduce. 0:10:28.368 --> 0:10:35.186 The main adaptation is what I said that beginning systems are good at the task they are trained. 0:10:35.186 --> 0:10:37.928 But how can we adapt them to new task? 0:10:38.959 --> 0:10:51.561 Document level is doing more context and we have two lectures about speech translation, 0:10:51.561 --> 0:10:56.859 so mostly before we are translating. 0:10:57.117 --> 0:11:00.040 Are now translating audio things. 0:11:00.040 --> 0:11:05.371 We have just additional challenges and these we will address. 0:11:10.450 --> 0:11:22.165 So to the motivation, why should you work on the theme translation and why should you 0:11:22.165 --> 0:11:23.799 put effort? 0:11:24.224 --> 0:11:30.998 So we want or we are living in a more global society. 0:11:30.998 --> 0:11:37.522 You have now the chance to communicate with people. 0:11:37.897 --> 0:11:44.997 And the danger of course is that languages are dying, and more and more languages are 0:11:44.997 --> 0:11:45.988 going away. 0:11:46.006 --> 0:11:53.669 I think at least that some opportunity in order to keep more languages is that we have 0:11:53.669 --> 0:12:01.509 technology solutions which help you to speak in your language and still communicate with 0:12:01.509 --> 0:12:04.592 people who speak another language. 0:12:04.864 --> 0:12:16.776 And on the one hand there is the need and more and more people want to speak in some 0:12:16.776 --> 0:12:19.159 other languages. 0:12:19.759 --> 0:12:27.980 For example, Iceland was really keen on getting Icelandic into commercial systems and they 0:12:27.980 --> 0:12:36.471 even provided data and so on because they wanted that their language is spoken longer and not 0:12:36.471 --> 0:12:38.548 just people switching. 0:12:38.959 --> 0:12:47.177 So there's even like yeah, they were spending for promoting this language in order to have 0:12:47.177 --> 0:12:55.125 all these digital tools available for languages which are not spoken by so many people. 0:12:56.156 --> 0:13:07.409 So it's questionable and it's not completely clear technology always provides. 0:13:10.430 --> 0:13:25.622 If we think about machine translation, there are different use cases in which you can use 0:13:25.622 --> 0:13:26.635 that. 0:13:27.207 --> 0:13:36.978 And this has some characteristics: So typically in this case it is where machine translation 0:13:36.978 --> 0:13:40.068 was used first anybody. 0:13:40.780 --> 0:13:50.780 Because most youth outlets around the world report at least some of the same events, like 0:13:50.780 --> 0:13:58.669 was probably covered around the world in a lot of different languages. 0:13:59.279 --> 0:14:08.539 That is one point yes, so the training gator is there. 0:14:08.539 --> 0:14:16.284 That's definitely a good point here and then. 0:14:17.717 --> 0:14:19.425 Yes, there was my regional idea. 0:14:19.425 --> 0:14:23.256 The motivation program was a bit different by you, but it's a good point. 0:14:23.256 --> 0:14:26.517 So on the one end you'll understand maybe not perfect English. 0:14:26.517 --> 0:14:30.762 Also, it's for his personal use, so you're using machine translation for you use. 0:14:31.311 --> 0:14:37.367 It's not as important that this is really perfect written text, but you're more interested 0:14:37.367 --> 0:14:38.564 in understanding. 0:14:38.858 --> 0:14:45.570 Maybe it's more clearer if you think about the other situation where it's about dissimination 0:14:45.570 --> 0:14:48.926 that means producing text in another language. 0:14:48.926 --> 0:14:55.138 So just imagine you have a website or you have a restaurant and you want to offer your 0:14:55.138 --> 0:14:55.566 menu. 0:14:56.476 --> 0:15:01.948 And in this case maybe you want to have a higher quality because in some of your. 0:15:01.901 --> 0:15:06.396 You're presenting something of yourself and you want to have good quality. 0:15:06.396 --> 0:15:11.490 Just remember you're writing a letter and if you're translating your letter then you 0:15:11.490 --> 0:15:17.123 don't want to have it full of mistakes because it's somehow a bad, bad oppression but if it's 0:15:17.123 --> 0:15:20.300 assimilation it's about you getting the information. 0:15:20.660 --> 0:15:25.564 So here you want your disciplination, you're producing texts for another language. 0:15:26.006 --> 0:15:31.560 And then you have the disadvantage that you maybe want to have a higher quality. 0:15:31.831 --> 0:15:43.432 Therefore, typically there is less amount, so normally you're getting more information 0:15:43.432 --> 0:15:46.499 than you're producing. 0:15:49.109 --> 0:15:57.817 Then of course there is a dynamic scenario where there is some type of interaction and 0:15:57.817 --> 0:16:07.099 the one thing which is interesting about the dialogue scenario is there is: So if you're 0:16:07.099 --> 0:16:18.045 translating a website you have all the data available but in a dialogue scenario you. 0:16:18.378 --> 0:16:23.655 And we'll see that in speech recognition this is a big challenge. 0:16:23.655 --> 0:16:30.930 Just to mention German where in German the work is often more at the end, so each harmony. 0:16:32.052 --> 0:16:36.343 Know that you want to generate the English sentence. 0:16:36.343 --> 0:16:42.740 Now you need to know if you cancel this registration to produce a second word. 0:16:42.740 --> 0:16:49.785 So you have to either guess or do something in order to provide the translation before 0:16:49.785 --> 0:16:52.052 the translation is already. 0:16:57.817 --> 0:17:00.530 The question, of course, is in the new world. 0:17:00.530 --> 0:17:05.659 I mean, of course, we can, on the one hand, say we don't want to have English, but the 0:17:05.659 --> 0:17:10.789 question is do we really need that many languages and how many are here at the moment? 0:17:11.291 --> 0:17:20.248 Does anybody have an idea how many languages are spoken in the world? 0:17:23.043 --> 0:17:26.510 This is already the first big challenge. 0:17:26.510 --> 0:17:34.120 What a language is and what no language is is already difficult, and then maybe one point 0:17:34.120 --> 0:17:40.124 people have to argue first about written language or spoken languages. 0:17:40.400 --> 0:17:47.765 For written languages I think that number is still too low, but for a spoken language 0:17:47.765 --> 0:17:53.879 people normally think: So you see that it's really a lot of languages which will be difficult 0:17:53.879 --> 0:17:54.688 to all happen. 0:17:55.035 --> 0:18:00.662 And these are just like you see Europe where there's relatively few languages. 0:18:00.662 --> 0:18:05.576 You already have quite a lot of languages, even walls and countries. 0:18:06.126 --> 0:18:13.706 Of course sometimes you share the language, but then you have Briton or Gillesian vest 0:18:13.706 --> 0:18:17.104 where you have languages in a country. 0:18:18.478 --> 0:18:24.902 And yeah, of course, there's the question: When does it start to be a language? 0:18:24.902 --> 0:18:27.793 And when is it more like a dialect? 0:18:27.793 --> 0:18:28.997 So is Catalan? 0:18:28.997 --> 0:18:31.727 Is Swiss German a known language? 0:18:31.727 --> 0:18:33.253 Or is it the same? 0:18:33.293 --> 0:18:36.887 So then, of course, it's are like Czech and Slovakian. 0:18:36.887 --> 0:18:42.704 I know heard that people can understand each other so they can just continue talking and 0:18:42.704 --> 0:18:45.711 understand by some of their own language and. 0:18:46.026 --> 0:18:56.498 Of course, it's partly also like about your own nationality, so I think some people said 0:18:56.498 --> 0:18:57.675 creation. 0:18:58.018 --> 0:19:04.957 But think for a lot of people you shouldn't say that they are part of being creation language. 0:19:05.165 --> 0:19:10.876 But you see therefore that it is not completely clear that there is no hardwater between this 0:19:10.876 --> 0:19:13.974 and the new language, and this is a different one. 0:19:14.094 --> 0:19:19.403 And of course it's getting more fluent when you talk about scientific things. 0:19:19.403 --> 0:19:25.189 I guess sometimes it's no longer clear if it's German or English because we start to 0:19:25.189 --> 0:19:27.707 use a lot of English terms in there. 0:19:27.707 --> 0:19:31.519 So of course there's interesting mixes which will talk. 0:19:33.193 --> 0:19:38.537 So should everybody just speak English, and these numbers are a bit older, have to admit: 0:19:38.938 --> 0:19:47.124 However, I don't think they're completely different now and it says like how many people know in 0:19:47.124 --> 0:19:54.718 Europe can speak English for countries where English is not the mothertown or for people. 0:19:54.995 --> 0:20:06.740 In some countries like smaller ones, for smaller countries you have quite high numbers. 0:20:07.087 --> 0:20:13.979 However, there are many countries where you have like twenty to thirty percent of the population, 0:20:13.979 --> 0:20:16.370 only being able to speak English. 0:20:16.370 --> 0:20:22.559 So if we would only do everything only in English, we would exclude half the population 0:20:22.559 --> 0:20:23.333 of Europe. 0:20:23.563 --> 0:20:30.475 And therefore providing translations is very important and therefore, for example, the European 0:20:30.475 --> 0:20:35.587 Parliament puts a really large amount of money into doing translation. 0:20:35.695 --> 0:20:40.621 So that's why you can speak in your mother too in the European Parliament. 0:20:40.621 --> 0:20:46.204 Everybody like everyone elected there can speak in there and they were translated to 0:20:46.204 --> 0:20:52.247 all the other languages and it's a huge effort and so the question is can we do better with 0:20:52.247 --> 0:20:52.838 machine. 0:20:53.493 --> 0:20:58.362 And for other countries things are even more. 0:20:58.362 --> 0:21:05.771 They may be not worse, difficult, but they are even more challenging. 0:21:06.946 --> 0:21:13.764 So there's even more diversity of languages and it might be even more important to do machines. 0:21:16.576 --> 0:21:31.034 If you see how many people speak French, Portuguese or English, it's relatively few compared to 0:21:31.034 --> 0:21:33.443 the population. 0:21:33.813 --> 0:21:46.882 So think that this should be around millions would understand you, but all the others wouldn't. 0:21:49.289 --> 0:21:54.877 So it seems to be very important to provide some taebo translation. 0:21:54.877 --> 0:21:58.740 It's a quite big industry as a European Union. 0:21:58.740 --> 0:22:05.643 This is already also quite long ago, but it won't get less spent like in that year. 0:22:05.643 --> 0:22:08.931 One point three billion on translation. 0:22:09.289 --> 0:22:21.315 So it might be very helpful to have tools in order to provide them, and as said, not 0:22:21.315 --> 0:22:26.267 all directions might be important. 0:22:26.426 --> 0:22:35.059 Is even not possible for students, so in the European Parliament they don't have all combinations 0:22:35.059 --> 0:22:36.644 of the different. 0:22:36.977 --> 0:22:42.210 And language is so if they want to translate from Maltese to Estonian or so. 0:22:42.402 --> 0:22:47.361 And maybe they have a translator for that, but there are some directions which don't have 0:22:47.361 --> 0:22:47.692 that. 0:22:47.692 --> 0:22:52.706 Then they handle directly, but they would translate first to French, German or or English, 0:22:52.706 --> 0:22:57.721 and then there would be a second translator getting the translation and really translating 0:22:57.721 --> 0:22:59.154 to your Italian language. 0:22:59.299 --> 0:23:06.351 And it's not always English, so they are really selecting what is most helpful. 0:23:06.351 --> 0:23:13.931 But you see that even in this small setup, with this large amount of effort in there, 0:23:13.931 --> 0:23:17.545 there's not enough ability to translate. 0:23:19.819 --> 0:23:21.443 And of course this was text. 0:23:21.443 --> 0:23:26.538 Then you have a lot of other things where you want to, for example, do speech translation. 0:23:26.538 --> 0:23:31.744 There is a lot of conferences which currently are all held in English, which of course might 0:23:31.744 --> 0:23:35.831 also not be the best solution if you've gone to some of the conferences. 0:23:36.176 --> 0:23:45.964 You might have heard some accented speech where people speak a language that is very 0:23:45.964 --> 0:23:49.304 different from their mother. 0:23:49.749 --> 0:23:52.059 Might be difficult to understand. 0:23:52.212 --> 0:23:59.123 We're currently having an effort for example by ACL, which is the conference organized in 0:23:59.123 --> 0:24:06.112 this field to provide these translations into ten hour languages so that also students who 0:24:06.112 --> 0:24:06.803 are not. 0:24:06.746 --> 0:24:12.446 That familiar English is able to read the papers and watch the present case. 0:24:16.416 --> 0:24:25.243 So the question is what can you do here and one interesting solution which we'll cover 0:24:25.243 --> 0:24:26.968 in this lecture? 0:24:27.087 --> 0:24:38.112 This always comes with a question: is it will it replace the human? 0:24:38.112 --> 0:24:40.382 And yes, the. 0:24:40.300 --> 0:24:49.300 Idea, but the question doesn't really happen and I'm any skeptical about that. 0:24:49.300 --> 0:24:52.946 So currently we are not seeing. 0:24:53.713 --> 0:24:55.807 So much more effort needed. 0:24:55.807 --> 0:25:00.294 Of course, machine translation is now used as some type of. 0:25:01.901 --> 0:25:11.785 If you think about in the European Parliament, they will have some humans doing their translation 0:25:11.785 --> 0:25:18.060 because: If you think about the chancel of Germany trembling somewhere and quite sure 0:25:18.060 --> 0:25:18.784 you want,. 0:25:19.179 --> 0:25:31.805 And so it's more like we are augmenting the possibilities to have more possibilities to 0:25:31.805 --> 0:25:37.400 provide translation and travel around. 0:25:39.499 --> 0:25:53.650 How can this technology help so machine translation is one way of dealing with? 0:25:54.474 --> 0:26:01.144 Of course, there is other tasks which do even without machine translation. 0:26:01.144 --> 0:26:04.613 Just think about summarize my lecture. 0:26:04.965 --> 0:26:08.019 Approaches doing that what they call end to end. 0:26:08.019 --> 0:26:11.635 So you just put an English text and get a German summary. 0:26:11.635 --> 0:26:17.058 However, a good baseline and an important thing is to either first lecture into German 0:26:17.058 --> 0:26:22.544 and then do a summary art, first do a summary in English and then translation language. 0:26:23.223 --> 0:26:28.764 Translation is very important in order to different application scenarios. 0:26:28.764 --> 0:26:33.861 We have that dissemination dialogue but also information extraction. 0:26:33.861 --> 0:26:39.993 So if you want to do like get information not only from English websites but from. 0:26:40.300 --> 0:26:42.427 Very different websites. 0:26:42.427 --> 0:26:46.171 It's helpful to have this type of solution. 0:26:50.550 --> 0:26:52.772 Yeah, what can you translate? 0:26:52.772 --> 0:26:59.660 Of course, we will focus on text, as I said for most of them, because it's about translation 0:26:59.660 --> 0:27:06.178 and anything first translates to text, and then change to text, and then we can do text 0:27:06.178 --> 0:27:07.141 translation. 0:27:09.189 --> 0:27:19.599 And text is not equals text, so we can do translation that is some of the most common. 0:27:19.499 --> 0:27:27.559 Is working on translation, so just imagine you are developing your new. 0:27:27.947 --> 0:27:34.628 Nowadays you don't want to have to only be available in English or German books in as 0:27:34.628 --> 0:27:40.998 many languages as possible, and if you use the standard tools it's not that easy. 0:27:41.141 --> 0:27:50.666 We have a different type of domain and there again we have very few contexts. 0:27:50.666 --> 0:27:56.823 Normally we translate: To pick up an app you have the menu and there's like safe. 0:27:57.577 --> 0:28:02.535 And then you only have safe. 0:28:02.535 --> 0:28:14.845 How should translate safe should it be written or should it be spicing? 0:28:16.856 --> 0:28:24.407 Then, of course, if you have like files, it might be that you have meta data to transport. 0:28:26.466 --> 0:28:27.137 Novels. 0:28:27.137 --> 0:28:32.501 Some work on that, but yeah, that's always a typical criticism. 0:28:32.501 --> 0:28:36.440 You'll never be able to translate Shakespeare. 0:28:36.656 --> 0:28:43.684 Think this is somehow the last use case of machine translation. 0:28:43.684 --> 0:28:47.637 For a translation of books there's. 0:28:47.847 --> 0:28:57.047 But the nice thing about machine translation is that it can translate to things which are 0:28:57.047 --> 0:29:05.327 boring, so think about translating some bureaucrative forms or some regulations. 0:29:05.565 --> 0:29:11.302 This is normally not very interesting, it's very repetitive, so their automation works 0:29:11.302 --> 0:29:11.697 well. 0:29:11.931 --> 0:29:17.519 Of course, there is also translations on Paibos images. 0:29:17.519 --> 0:29:24.604 I guess you point your camera to an object where it translates things. 0:29:25.005 --> 0:29:43.178 And we'll cover that at the end, as said, the speech translation. 0:29:43.663 --> 0:29:46.795 So you can't provide the translation of the lecture. 0:29:46.795 --> 0:29:50.518 If I'm five slides further then you would see the translation. 0:29:50.518 --> 0:29:52.291 It might not be very helpful. 0:29:54.794 --> 0:29:57.062 We are not speaking as we are written. 0:29:57.062 --> 0:29:59.097 It's again like a domain mismatch. 0:29:59.359 --> 0:30:10.161 So typically the sentences are not full sentences and I'm saying this is not the right way to 0:30:10.161 --> 0:30:19.354 praise it and if you just read what was written it might be hard to understand. 0:30:23.803 --> 0:30:36.590 We are focusing on the first application scenario that is fully out of management. 0:30:37.177 --> 0:30:46.373 Of course, there are quite interesting application scenarios for other things where it should 0:30:46.373 --> 0:30:47.645 be referred. 0:30:47.867 --> 0:30:49.695 Where it's no longer going to be. 0:30:49.695 --> 0:30:52.436 We have this tool and it works, but it's a market. 0:30:52.436 --> 0:30:57.381 We have the machine translation system and the human translator, and they somehow cooperate 0:30:57.381 --> 0:30:59.853 and try to be as fast as possible in doing a. 0:31:00.380 --> 0:31:12.844 The easiest idea there would be the first point you take the machine translation. 0:31:13.553 --> 0:31:17.297 That sometimes farther might not be the best way of suing it. 0:31:17.357 --> 0:31:25.308 Any ideas or what else you could do, then maybe the machine could aid the human and say 0:31:25.308 --> 0:31:27.838 I'm sure about this author. 0:31:28.368 --> 0:31:32.319 Yeah, very interesting, very good. 0:31:32.319 --> 0:31:42.252 Of course, the dangerous thing there is you asking something from a machine translation 0:31:42.252 --> 0:31:45.638 system where it's really bad. 0:31:45.845 --> 0:31:50.947 There is quality estimation that maybe it will couple that in evaluation so in evaluation 0:31:50.947 --> 0:31:55.992 you know what is correct translation and you have another output and you try to estimate 0:31:55.992 --> 0:31:57.409 how good is the quality. 0:31:57.409 --> 0:32:02.511 In quality estimation you don't have you only have a source and time and good question is 0:32:02.511 --> 0:32:03.531 exactly this one. 0:32:03.531 --> 0:32:05.401 Is it a good translation or not? 0:32:05.665 --> 0:32:12.806 This might be easier because the system might not know what translation is. 0:32:13.053 --> 0:32:23.445 Human is very good at that for machines that are difficult, but of course that's an interesting 0:32:23.445 --> 0:32:24.853 application. 0:32:25.065 --> 0:32:32.483 Be more interactive so that you may be translating if the human changes the fifth word. 0:32:32.483 --> 0:32:36.361 What does it mean for the remaining sentence? 0:32:36.361 --> 0:32:38.131 Do I need to change? 0:32:38.131 --> 0:32:43.948 There are also things like you don't have to repeat the same errors. 0:32:47.767 --> 0:32:57.651 Hell our automated basemen, you only want to correct at once and not at all positions. 0:33:00.000 --> 0:33:21.784 And then they ask, for example, so before the translation is done they ask: I'm not directly 0:33:21.784 --> 0:33:23.324 aware of that. 0:33:23.324 --> 0:33:33.280 I think it's a good way of ending and I think it's where, especially with more advanced dialogue 0:33:33.280 --> 0:33:34.717 strategy and. 0:33:35.275 --> 0:33:38.831 Currently think of most of the focus is like at least determining. 0:33:39.299 --> 0:33:45.646 Don't have this information that is already challenging, so there is quite some work on 0:33:45.646 --> 0:33:49.541 quality estimation that I'm missing your information. 0:33:49.789 --> 0:33:53.126 But is there something missing? 0:33:53.126 --> 0:33:59.904 It's really quite challenging and think that is where currently. 0:34:00.260 --> 0:34:05.790 What is there is there is opportunities to provide or there is models to directly provide 0:34:05.790 --> 0:34:06.527 additional? 0:34:06.786 --> 0:34:13.701 You can give them anything you have and provide them. 0:34:13.701 --> 0:34:21.129 It's a similar situation if you're translating to German. 0:34:21.641 --> 0:34:31.401 And it would just guess normally or do some random guessing always means it's using some 0:34:31.401 --> 0:34:36.445 information which should not be really there. 0:34:36.776 --> 0:34:46.449 So then you can provide it with an additional input or you should use formula or non formula. 0:34:47.747 --> 0:35:04.687 To know that this information is missing. 0:35:04.544 --> 0:35:19.504 Since you're not specifically modeling this, it's likely that there is a gender difference 0:35:19.504 --> 0:35:21.805 in languages. 0:35:26.046 --> 0:35:39.966 One are we doing good search on machine translation, so it's a very important part to ask in natural 0:35:39.966 --> 0:35:42.860 language processing. 0:35:43.283 --> 0:35:49.234 So of course you have a lot of computer science thing in there and that's the backbone of. 0:35:49.569 --> 0:36:01.848 However, task and understanding you can also get from information like computational linguistics, 0:36:01.848 --> 0:36:08.613 which tell you about what language it's good to know. 0:36:08.989 --> 0:36:15.425 Doesn't mean that in a computer we have to bottle it exactly the same, but for example 0:36:15.425 --> 0:36:22.453 to know that there is something like morphology, which means how words are built, and that for 0:36:22.453 --> 0:36:24.746 some languages it's very easy. 0:36:24.746 --> 0:36:28.001 In English there is nearly no worth coming. 0:36:28.688 --> 0:36:35.557 Well in Germany you already start for soon you have like different forms and so on. 0:36:36.316 --> 0:36:41.991 And for other languages, for finish, it's even more complicated with Basque. 0:36:41.991 --> 0:36:44.498 I think for some words more than. 0:36:45.045 --> 0:36:52.098 So knowing this, of course, gives you some advice. 0:36:52.098 --> 0:37:04.682 How do I look at that now because we'll see in the basic treat each word as an individual? 0:37:06.106 --> 0:37:09.259 Of course there is a lot of interest also prone from industry. 0:37:09.259 --> 0:37:10.860 There is a lot of applications. 0:37:11.191 --> 0:37:17.068 There's research groups at Google, Facebook, and Amazon. 0:37:17.068 --> 0:37:26.349 So there's quite a lot of interest in providing that for German and English it is solved. 0:37:26.546 --> 0:37:27.569 Annoucing it's hard. 0:37:27.569 --> 0:37:31.660 We're saying that not hard, but of course we haven't acquired high quality in them. 0:37:32.212 --> 0:37:39.296 But there's currently really a large trend in building other systems for low research 0:37:39.296 --> 0:37:40.202 languages. 0:37:40.480 --> 0:37:53.302 So there are tasks on last year's task on translating from Native American languages: 0:37:53.193 --> 0:37:58.503 Don't know yet but but five other languages, so how can you translate from them? 0:37:58.538 --> 0:38:05.074 Then you don't have like millions of sentences, but you might have only the Bible or some more 0:38:05.074 --> 0:38:05.486 data. 0:38:05.486 --> 0:38:08.169 Then the question is, what can you do? 0:38:08.169 --> 0:38:09.958 And how good can you get? 0:38:14.794 --> 0:38:17.296 One thing is very important. 0:38:17.296 --> 0:38:25.751 Of course, in a lot of A I is to measure the quality and what you can measure is quite important. 0:38:25.986 --> 0:38:37.213 So that's why for many years of regular there is different evaluation campaigns where people 0:38:37.213 --> 0:38:38.178 submit. 0:38:39.419 --> 0:38:45.426 We're often part of the statistical machine translation original, yet now I think it's 0:38:45.426 --> 0:38:51.019 a machine translation where it's mostly about European languages and used texts. 0:38:51.051 --> 0:38:57.910 The International Workshop of Spoken Language Translation, which is translation about lectures 0:38:57.910 --> 0:39:04.263 which we are co organizing, and there is a bovia as I said building strong systems this 0:39:04.263 --> 0:39:04.696 time. 0:39:04.664 --> 0:39:11.295 This has established translating conference presentations from English into ten different 0:39:11.295 --> 0:39:17.080 languages: And then, of course, you have to deal with things like special vocabulary. 0:39:17.037 --> 0:39:23.984 You think about recurrent real networks are terms like co-recurrent networks, convolutional 0:39:23.984 --> 0:39:24.740 networks. 0:39:25.545 --> 0:39:29.917 That might be more difficult to translate and you also have to decide who I need to translate 0:39:29.917 --> 0:39:33.359 or should I keep it in English, and that's not the same in each language. 0:39:33.873 --> 0:39:37.045 In German maybe mostly you keep it. 0:39:37.045 --> 0:39:44.622 I think in French people are typically like wanting to translate as much as possible. 0:39:44.622 --> 0:39:52.200 These are then challenges and then, of course, in Poland where it's also challenging. 0:39:53.153 --> 0:39:59.369 I think all of the speakers in the test that are not native in your speakers, so you need 0:39:59.369 --> 0:40:05.655 to translate people with a German accent or with a French accent or with a Japanese accent 0:40:05.655 --> 0:40:09.178 or an English accent, which poison has additional. 0:40:12.272 --> 0:40:21.279 Yes, so there is criticism always with new technologies because people say will never 0:40:21.279 --> 0:40:23.688 translate Shakespeare. 0:40:24.204 --> 0:40:26.845 Partly agree with the second. 0:40:26.845 --> 0:40:34.682 Maybe it's not good at translating Shakespeare, but there's many people working on that. 0:40:35.255 --> 0:40:38.039 Of course, the poison cookie is a challenge. 0:40:38.858 --> 0:40:44.946 The thing is here that the cookie chart that you can't never be sure if the machine translation 0:40:44.946 --> 0:40:47.546 system doesn't really mistake somewhere. 0:40:47.546 --> 0:40:53.316 So if you can't be sure that there's no error in there, how can you trust the translation? 0:40:55.275 --> 0:41:01.892 That is partly true, on the other hand, otherwise you have to translate to a human translator 0:41:01.892 --> 0:41:06.116 and men who are sometimes overestimating human performance. 0:41:06.746 --> 0:41:15.111 They are very good translators but under a lot of pressure and not human translations. 0:41:15.715 --> 0:41:22.855 The question is: When can you trust it enough anyway? 0:41:22.855 --> 0:41:28.540 You should be careful about trusting them. 0:41:31.011 --> 0:41:38.023 And I think some of them are too old now because it has been shown that it is helpful to have 0:41:38.023 --> 0:41:41.082 some type of machine translation system. 0:41:41.082 --> 0:41:47.722 Of course, it is not buying the car, so typically still a system is not working forever. 0:41:48.048 --> 0:41:56.147 If you want your dedicated system, which is good for the task you are, they are typically 0:41:56.147 --> 0:41:57.947 not as generalized. 0:41:58.278 --> 0:42:07.414 That can translate news and chats, and I don't know what. 0:42:07.414 --> 0:42:12.770 So typically if you want to show. 0:42:12.772 --> 0:42:18.796 It's not made for, it has not seen very well and then you see a bad quality. 0:42:19.179 --> 0:42:27.139 But that's also like yeah, therefore you don't build it. 0:42:27.139 --> 0:42:42.187 If you have a sports car and you are driving off road you should: Yeah, you can also say 0:42:42.187 --> 0:42:49.180 the other way around trans machine translation is already solved, and especially with more 0:42:49.180 --> 0:42:50.487 people think so. 0:42:50.750 --> 0:43:04.275 However, there is an impressive performance of machine translation, but it's not stated 0:43:04.275 --> 0:43:06.119 of the art. 0:43:06.586 --> 0:43:11.811 And yeah, they're good for some domains and some languages that are even like already. 0:43:12.572 --> 0:43:27.359 Have Microsoft has a very super human performance claiming that their machine translated system. 0:43:27.467 --> 0:43:38.319 However, there was one domain use and some language in Spanish where there is a huge amount 0:43:38.319 --> 0:43:45.042 of training data and you can build a very strong system. 0:43:45.505 --> 0:43:48.605 And you even don't have to go to these extreme cases. 0:43:48.688 --> 0:43:54.328 We have worked on Canada, which is a language in India spoken. 0:43:54.328 --> 0:44:01.669 I think by also around eighty million people so similar to to German that it has. 0:44:01.669 --> 0:44:07.757 The quality is significantly worse, it has significantly less data. 0:44:08.108 --> 0:44:15.132 There are still quite a lot of languages where the quality is not, where you want to have. 0:44:15.295 --> 0:44:17.971 Scaling this is not as easy at this thing. 0:44:17.971 --> 0:44:23.759 That's why we're also interested in multilingual systems with the hope that we don't have to 0:44:23.759 --> 0:44:29.548 build a system for each possible combination, but we can build a system which can cover many 0:44:29.548 --> 0:44:33.655 tags, many languages and then also need less data for each other. 0:44:39.639 --> 0:44:51.067 With invasion maybe some presentation of everything is a bit cat that can say the most important. 0:44:51.331 --> 0:45:09.053 So machine translation started coming from information theory in there was this: It's 0:45:09.053 --> 0:45:13.286 treating machine translation as encryption or decryption. 0:45:13.533 --> 0:45:21.088 Don't understand it, want to have it in English, treat it as if it's like encrypted English, 0:45:21.088 --> 0:45:28.724 and then apply my decryption algorithm, which they were working a lot during the Second World 0:45:28.724 --> 0:45:29.130 War. 0:45:29.209 --> 0:45:34.194 And so if I cannot do this detruction then this sings a song. 0:45:34.934 --> 0:45:42.430 And they based on that they had rules and so on. 0:45:42.430 --> 0:45:50.843 So they had the judge Georgetown experiments in where. 0:45:51.691 --> 0:45:57.419 From English and then they were like wow. 0:45:57.419 --> 0:46:01.511 This is solved in some years. 0:46:01.511 --> 0:46:04.921 Now we can do sentences. 0:46:06.546 --> 0:46:18.657 As you can imagine this didn't really work out that way, so it's not really happening. 0:46:18.657 --> 0:46:24.503 The spirit is willing, but flesh is weak. 0:46:24.444 --> 0:46:30.779 Translated it to Russian and then to Germany and then vodka is good but the meat is rotten. 0:46:31.271 --> 0:46:39.694 Think it never really happened this way, but you can see you can imagine that something 0:46:39.694 --> 0:46:49.533 like that could happen, and then in in the there was this report saying: It's more challenging 0:46:49.533 --> 0:46:56.877 than expected and the problem is that we have to invest more. 0:46:56.877 --> 0:47:02.801 There's no benefit for doing machine translation. 0:47:04.044 --> 0:47:09.255 At least in some other countries there was a bit, but then for some time there wasn't 0:47:09.255 --> 0:47:10.831 that big out of progress. 0:47:12.152 --> 0:47:26.554 We have then in the' 70s there were some rule based systems that would cover out some linguistic 0:47:26.554 --> 0:47:28.336 background. 0:47:28.728 --> 0:47:34.013 They are now doing very good machine translation, but they had a really huge rule base. 0:47:34.314 --> 0:47:43.538 So they really have like handwritten roots how to parse sentences, how to translate parse 0:47:43.538 --> 0:47:45.587 sentences to parse. 0:47:46.306 --> 0:47:55.868 When which word should be translated, these rule based systems were quite strong for a 0:47:55.868 --> 0:47:57.627 very long time. 0:47:57.917 --> 0:48:03.947 So even in or so for some language fares and some remains, it was better than a machine 0:48:03.947 --> 0:48:04.633 learning. 0:48:05.505 --> 0:48:09.576 Well, of course, there was a lot of effort in and a lot of experts were building this. 0:48:11.791 --> 0:48:13.170 And then. 0:48:13.053 --> 0:48:18.782 The first statistical machine translations were coming in the early nineties. 0:48:18.782 --> 0:48:25.761 There's the system by IBM will refer to them as a T by the IBM models, which are quite famous, 0:48:25.761 --> 0:48:32.886 and they were used to film your machine translations from the nineties nineties to two thousand. 0:48:32.912 --> 0:48:35.891 Fifteen or so people were working on the IBM models. 0:48:36.496 --> 0:48:44.608 And that was the first way of doing a machine translation with statisticals or machine learning. 0:48:44.924 --> 0:48:52.143 And it was possible through the French English under a corpusol from the Canadian Parliament 0:48:52.143 --> 0:48:59.516 they also had proceedings in French and English and people tried to use that to translate and. 0:49:01.681 --> 0:49:06.919 And yes, so that was than the start of statistical machine translation. 0:49:07.227 --> 0:49:17.797 Is called a phrase page machine translation was introduced where you could add more information 0:49:17.797 --> 0:49:26.055 in use longer chunks to translate and phrase page translation was somehow. 0:49:26.326 --> 0:49:27.603 She'll Start Fourteen. 0:49:27.767 --> 0:49:37.721 With this straight space machine sensation we saw the first commercial systems. 0:49:38.178 --> 0:49:45.301 And yeah, that was the first big advantage where really you can see the machine translation. 0:49:47.287 --> 0:49:55.511 And neural machine translation was mainly introduced. 0:49:55.511 --> 0:50:07.239 That means there was a shift from traditional statistical modeling to using. 0:50:07.507 --> 0:50:09.496 And that was quite impressive. 0:50:09.496 --> 0:50:11.999 It was really within one or two years. 0:50:11.999 --> 0:50:17.453 The whole research community shifted from what they had been working on since twenty 0:50:17.453 --> 0:50:17.902 years. 0:50:17.902 --> 0:50:23.485 And everybody was using this pattern, you know networks, because just the performances 0:50:23.485 --> 0:50:25.089 were really really much. 0:50:25.425 --> 0:50:35.048 Especially they are what we also see now with chat boards like the impressive thing. 0:50:35.135 --> 0:50:45.261 That was very, very challenging if you see machine translation before that, especially 0:50:45.261 --> 0:50:47.123 if the English. 0:50:47.547 --> 0:50:53.352 But if you were transmitting to German you would see that the agreement so that it's there 0:50:53.352 --> 0:50:58.966 shown abound and dishewn and boima and this didn't always really work perfect maybe for 0:50:58.966 --> 0:51:04.835 the short range of work but then it has to be accusative and it's like far away then things 0:51:04.835 --> 0:51:06.430 didn't really work well. 0:51:06.866 --> 0:51:13.323 Now with new machine translation we have a bit of a different problem: So the sentences 0:51:13.323 --> 0:51:16.901 are typically really nice. 0:51:16.901 --> 0:51:24.056 They are perfectly written not always but very often. 0:51:24.224 --> 0:51:36.587 So that adequacy and their conveillance should have the same meaning is typically the bigger. 0:51:42.002 --> 0:51:46.039 So how can we do so last? 0:51:46.039 --> 0:51:54.889 What are the things and how can we do machine rendering? 0:51:55.235 --> 0:52:01.297 So we had first blue based systems, and as a side systems we did that we manually created 0:52:01.297 --> 0:52:01.769 rules. 0:52:01.861 --> 0:52:07.421 And there were rules how to dissemvy real ambiguities. 0:52:07.421 --> 0:52:16.417 For example, we had the word banks look at the context and do rules like to decide when. 0:52:17.197 --> 0:52:28.418 How to translate the structure, but you know how to transfer the structure that you work 0:52:28.418 --> 0:52:33.839 has to split it in German and move to the. 0:52:35.295 --> 0:52:36.675 Here's a difficult thing. 0:52:36.675 --> 0:52:39.118 My thing is you don't need any training data. 0:52:39.118 --> 0:52:41.295 It's not like now with machine learning. 0:52:41.295 --> 0:52:46.073 If you build a machine translation system, the first question you should ask is do I have 0:52:46.073 --> 0:52:46.976 data to do that? 0:52:46.976 --> 0:52:48.781 Do I have parallel data to train? 0:52:49.169 --> 0:52:50.885 Here there's no data. 0:52:50.885 --> 0:52:57.829 It's like all trades, pencils and roads, but the problem is people trading the roads and 0:52:57.829 --> 0:52:59.857 this needs to be experts. 0:52:59.799 --> 0:53:06.614 Understand at least the grammar in one language, basically the grammar in both languages. 0:53:06.614 --> 0:53:09.264 It needs to be a real language to. 0:53:10.090 --> 0:53:17.308 Then we have the two corpus based machine translation approaches, and then we use machine 0:53:17.308 --> 0:53:22.682 learning to learn how to translate from one language to the other. 0:53:22.882 --> 0:53:29.205 We should find out ourselves what is the meaning of individual words, which words translate 0:53:29.205 --> 0:53:30.236 to each other. 0:53:30.236 --> 0:53:36.215 The only information we give is the German sentence, the English sentence, and then we 0:53:36.215 --> 0:53:37.245 look for many. 0:53:37.697 --> 0:53:42.373 So maybe you think there's a Bible for each language. 0:53:42.373 --> 0:53:44.971 There shouldn't be a problem. 0:53:45.605 --> 0:53:52.752 But this is not the scale when we're talking about. 0:53:52.752 --> 0:54:05.122 Small systems have maybe one hundred thousand sentences when we're building large models. 0:54:05.745 --> 0:54:19.909 The statistical models do statistics about how the word screw occur and how often the 0:54:19.909 --> 0:54:21.886 word screw. 0:54:22.382 --> 0:54:29.523 While we were focused on it was currently most of the cases referred to as neural communication. 0:54:30.050 --> 0:54:44.792 So in this case the idea is that you have a neural model which is a big neural network. 0:54:45.345 --> 0:54:55.964 And for these machine drums there quite challenging tasks. 0:54:55.964 --> 0:55:03.883 For example, this transformal architecture. 0:55:03.903 --> 0:55:07.399 Cast by Google in two thousand eight. 0:55:08.028 --> 0:55:19.287 Here want to ask the screw-based machine translation of that part. 0:55:22.862 --> 0:55:33.201 Would say it's mainly rule based systems because purely rule based systems maybe exist with 0:55:33.201 --> 0:55:36.348 some very exotic languages. 0:55:36.776 --> 0:55:43.947 Of course, the idea of investigating if we have this type of rulers that might be still 0:55:43.947 --> 0:55:45.006 interesting. 0:55:45.105 --> 0:55:52.090 Maybe you can try to let someone force the rules in there. 0:55:52.090 --> 0:55:57.655 You might use rules to create artificial data. 0:55:57.557 --> 0:56:03.577 That it might be helpful to have some concepts which develop by bilinguistic researches to 0:56:03.577 --> 0:56:09.464 somehow interview that that's still an open question is sometimes helpful, and of course 0:56:09.464 --> 0:56:13.235 is also interesting from more the analyzed perspectives. 0:56:13.235 --> 0:56:13.499 So. 0:56:13.793 --> 0:56:20.755 Do the new networks have these types of concepts of gender or anything? 0:56:20.755 --> 0:56:23.560 And can we test that though? 0:56:30.330 --> 0:56:34.255 Yes, and then the other way of describing how this can be done. 0:56:34.574 --> 0:56:52.021 And then originally mainly for a rule based system that can be used for a lot of scenarios. 0:56:52.352 --> 0:57:04.135 In real ways, the first world has really direct translation systems that work for related languages. 0:57:04.135 --> 0:57:11.367 You mainly look at each word and replace the word by the one. 0:57:11.631 --> 0:57:22.642 Another idea is that you first do some type of animus on the source side, so for example 0:57:22.642 --> 0:57:28.952 you can create what is referred to as a path tree. 0:57:30.150 --> 0:57:36.290 Or you can instead, and that is what is called the lingua face approach. 0:57:36.290 --> 0:57:44.027 You take the short sentence and parse it into a semantic representation, which is hopefully 0:57:44.027 --> 0:57:44.448 the. 0:57:44.384 --> 0:57:50.100 Only of the meaning of what is said and then you can generate it to any other language because 0:57:50.100 --> 0:57:55.335 it has a meaning and then you can need a part generation which can generate all other. 0:57:57.077 --> 0:58:09.248 The idea is somewhat nice to have this type of interlingua, general representation of all 0:58:09.248 --> 0:58:17.092 meanings, and they always translate into the interlingua. 0:58:17.177 --> 0:58:19.189 A Little World and It's Been Somewhere. 0:58:20.580 --> 0:58:26.684 It shouldn't be a natural language because it shouldn't have ambiguities so that's a big 0:58:26.684 --> 0:58:32.995 difference so the story and the tiger language have ambiguities so the idea is they do some 0:58:32.995 --> 0:58:39.648 semantic representation or what does it mean and so on and therefore it's very easy to generate. 0:58:41.962 --> 0:58:45.176 However, that is a challenge that this really exists. 0:58:45.176 --> 0:58:48.628 You cannot define the language for anything in the world. 0:58:49.249 --> 0:58:56.867 And that's why the Lingo-based approach typically worked for small domains to do hotel reservation, 0:58:56.867 --> 0:59:00.676 but if you want to define the Lingo for anything. 0:59:01.061 --> 0:59:07.961 There have been approaches and semantics, but it's yeah, it's not really possible CR. 0:59:07.961 --> 0:59:15.905 So approaches to this because I mean a seasonal vector's face and bitch eyes and slaves everything 0:59:15.905 --> 0:59:20.961 that I mitonized that they all could end up in the same space. 0:59:21.821 --> 0:59:24.936 That is not the question. 0:59:24.936 --> 0:59:35.957 If you talk about neural networks, it's direct translation on the one you're putting in the 0:59:35.957 --> 0:59:36.796 input. 0:59:36.957 --> 0:59:44.061 And you can argue for both that we have been making this representation language agnostic 0:59:44.061 --> 0:59:45.324 or independent. 0:59:47.227 --> 0:59:52.912 Until now we were able to make it less language dependent but it's very hard to make it completely 0:59:52.912 --> 0:59:54.175 language independent. 0:59:54.175 --> 0:59:59.286 Maybe it's also not necessary and of course if there's again the problem there's not all 0:59:59.286 --> 1:00:04.798 information and the source and the target there is different types of information if you remove 1:00:04.798 --> 1:00:05.602 all language. 1:00:05.585 --> 1:00:09.408 Information might be that you have removed too many information. 1:00:10.290 --> 1:00:15.280 Talk about this and there's a very interesting research direction in which we are working 1:00:15.280 --> 1:00:20.325 on on the multilingual part because there is especially the case if we have several source 1:00:20.325 --> 1:00:25.205 languages, several type of languages who try to generate a representation in the middle 1:00:25.205 --> 1:00:27.422 which have the few language dependence. 1:00:32.752 --> 1:00:46.173 Yes, so for a direct base approach, so as said the first one is dictionary based approach. 1:00:46.806 --> 1:00:48.805 Replace some words with other words. 1:00:48.805 --> 1:00:51.345 Then you have exactly the same same structure. 1:00:51.771 --> 1:00:55.334 Other problems are one to one correspondence. 1:00:55.334 --> 1:01:01.686 Some phrases are expressed with several words in English, but one word in German. 1:01:01.686 --> 1:01:03.777 That's extremely the case. 1:01:03.777 --> 1:01:07.805 Just think about all our composites like the Donau. 1:01:08.608 --> 1:01:18.787 Which is used very often as been referred to as translation memory. 1:01:18.787 --> 1:01:25.074 It might seem very simple, but it's like. 1:01:26.406 --> 1:01:33.570 That means you might think of this not helpful at all, but you know think about translating. 1:01:33.513 --> 1:01:38.701 The law text is more like the interactive scenario for the human translator. 1:01:38.701 --> 1:01:44.091 In law text there is a lot of repetition and a lot of phrases occur very often. 1:01:44.424 --> 1:01:55.412 The translator has just a background of translation memory and retrieve all this translation. 1:01:55.895 --> 1:02:07.147 There is even another benefit in addition to less work: That is also precise in the way 1:02:07.147 --> 1:02:19.842 know this creates a small mistake in the North Carolina. 1:02:20.300 --> 1:02:22.584 By especially its like consistence,. 1:02:23.243 --> 1:02:32.954 If you once translate the sentence this way you again translate it and especially for some 1:02:32.954 --> 1:02:36.903 situations like a company they have. 1:02:37.217 --> 1:02:47.695 With this one, of course, you get more consistent translations. 1:02:47.695 --> 1:02:56.700 Each one is a style where phrases maybe are retrieved. 1:03:01.861 --> 1:03:15.502 Then we have these transfer based approaches where we have three steps: Analysts remain 1:03:15.502 --> 1:03:25.975 that you check one synthetic structure, so for example for morphology the basic. 1:03:26.286 --> 1:03:37.277 Then you will do a parstry or dependency structure that this is the adjective of the balm. 1:03:37.917 --> 1:03:42.117 Then you can do the transfer where you transfer the structure to the other. 1:03:42.382 --> 1:03:46.633 There you have to do, for example, it's re-ordering because the satisfaction is different. 1:03:46.987 --> 1:03:50.088 In German, the adjective is before the noun. 1:03:50.088 --> 1:03:52.777 In Spanish, it's the other way around. 1:03:52.777 --> 1:03:59.256 You have first found and then that it's nice and these types of rehonoring can be done there. 1:03:59.256 --> 1:04:04.633 You might have to do other things like passive voice to exit voice and so on. 1:04:05.145 --> 1:04:14.074 And in some type of lexical transverse it should like to me: And then you are doing the 1:04:14.074 --> 1:04:16.014 generation. 1:04:16.014 --> 1:04:25.551 Of course, you would do the agreement if it is accusative. 1:04:25.551 --> 1:04:29.430 What type of adjective? 1:04:30.090 --> 1:04:32.048 Is some kind of saving. 1:04:32.048 --> 1:04:39.720 Of course, here, because the analyze has only to be done in the source language, the transfer 1:04:39.720 --> 1:04:41.679 has to do on the pairs. 1:04:41.679 --> 1:04:48.289 But if you not look German, English and French through all directions, you only. 1:04:53.273 --> 1:04:59.340 Then there is an interlingua card which is really about the pure meaning, so you have 1:04:59.340 --> 1:05:00.751 a semantic grammar. 1:05:01.061 --> 1:05:07.930 To represent everything and one thing, one nice implication is more extreme than before. 1:05:07.930 --> 1:05:15.032 You don't have the transfer anymore, so if you add one language to it and you have already. 1:05:15.515 --> 1:05:26.188 If you add the one parting and the one generation phase, you can now translate from: So you need 1:05:26.188 --> 1:05:40.172 components which do the and components which do the generation, and then you can translate: 1:05:41.001 --> 1:05:45.994 You can also do other things like paraphrasing. 1:05:45.994 --> 1:05:52.236 You can translate back to the words language and hopefully. 1:05:53.533 --> 1:06:05.013 If you're sparkling trying to analyze it, it was also down a lot for ungrammetical speech 1:06:05.013 --> 1:06:11.518 because the idea is you're in this representation. 1:06:12.552 --> 1:06:18.679 Of course, it's very much work and it's only realistic for limited domains. 1:06:20.000 --> 1:06:25.454 Then we're, we're have the campus based approach. 1:06:25.745 --> 1:06:32.486 So we'll talk about a lot about peril layer and what is really peril data is what you know 1:06:32.486 --> 1:06:34.634 from the Rosetta stone page. 1:06:34.634 --> 1:06:41.227 That is, you have a sewer sentence and you have a target sentence and you know they need 1:06:41.227 --> 1:06:42.856 to watch translation. 1:06:43.343 --> 1:06:46.651 And that's important, so the alignment is typically at a sentence level. 1:06:46.987 --> 1:06:50.252 So you know, for each sentence what is a translation? 1:06:50.252 --> 1:06:55.756 Not always perfect because maybe there's two German sentences and one English, but at that 1:06:55.756 --> 1:06:57.570 level it's normally possible. 1:06:57.570 --> 1:07:03.194 At word level you can't do that because it's a very complicated thing and sense level that's 1:07:03.194 --> 1:07:04.464 normally a relative. 1:07:05.986 --> 1:07:12.693 Some type of machine learning which tries to learn dismapping between sentences on the 1:07:12.693 --> 1:07:14.851 English side and sentences. 1:07:15.355 --> 1:07:22.088 Of course this doesn't look like good mapping too complex but you try to find something like 1:07:22.088 --> 1:07:28.894 that where it's a very nice mapping so there's always the mixing things are met to each other 1:07:28.894 --> 1:07:32.224 and then if you have the English you can try. 1:07:32.172 --> 1:07:36.900 In another English sentence you can apply the same mannering and hopefully adhere to 1:07:36.900 --> 1:07:38.514 the right sentence in terms. 1:07:38.918 --> 1:07:41.438 The big problem here. 1:07:41.438 --> 1:07:44.646 How can we find this model? 1:07:44.646 --> 1:07:50.144 How to map English centers into German centers? 1:07:54.374 --> 1:08:08.492 How we do that is that we are trying to maximize the probability, so we have all the letterstone. 1:08:09.109 --> 1:08:15.230 Then we're having some type of model here which takes the Suez language and translates 1:08:15.230 --> 1:08:16.426 it for a target. 1:08:16.896 --> 1:08:34.008 And then we are in our translation, and we are adjusting our model in a way that the probability. 1:08:34.554 --> 1:08:48.619 How that is the idea behind it, how we are pushed now, implement that is part of the bottle. 1:08:51.131 --> 1:09:01.809 And then if we want to do translation, what we are doing is we are trying to find the translation. 1:09:01.962 --> 1:09:06.297 So we are scoring many possible translations. 1:09:06.297 --> 1:09:12.046 There is an infinite number of sentences that we are trying. 1:09:12.552 --> 1:09:18.191 That may be a bit of a problem when we talk about confidence because we are always trying 1:09:18.191 --> 1:09:19.882 to find the most probable. 1:09:20.440 --> 1:09:28.241 And then, of course, we are not really having intrinsically the possibility to say, oh, I 1:09:28.241 --> 1:09:31.015 have no idea in this situation. 1:09:31.015 --> 1:09:35.782 But our general model is always about how can we find? 1:09:40.440 --> 1:09:41.816 Think It's. 1:09:42.963 --> 1:09:44.242 Get Four More Slides. 1:09:46.686 --> 1:09:52.025 So just high level, so for a proper space this one we won't cover again. 1:09:52.352 --> 1:10:00.808 Its example based machine translation was at the beginning of SMT. 1:10:00.808 --> 1:10:08.254 The idea is that you take subparts and combine them again. 1:10:08.568 --> 1:10:11.569 So this will not be really covered here. 1:10:11.569 --> 1:10:15.228 Then the statistical machine translation we will. 1:10:17.077 --> 1:10:18.773 Yeah, we will cover next week. 1:10:19.079 --> 1:10:27.594 The idea is there that we automatically now, if we have the sentence alignment, we automatically. 1:10:27.527 --> 1:10:34.207 In the sentences, and then we can learn statistical models of how probable words are translated 1:10:34.207 --> 1:10:39.356 to each other, and then the surge is that we create different hypotheses. 1:10:39.356 --> 1:10:45.200 This could be a translation of this part, this could be a translation of that part. 1:10:45.200 --> 1:10:47.496 We give a score to each of them. 1:10:47.727 --> 1:10:51.584 The statistical machine manual is where a lot of work is done. 1:10:51.584 --> 1:10:54.155 How can we score how good translation is? 1:10:54.494 --> 1:11:04.764 The words can recur this type of structure, how is it reordered, and then based on that 1:11:04.764 --> 1:11:08.965 we search for the best translation. 1:11:12.252 --> 1:11:19.127 Then yeah, that one what we'll cover most of the time is is a neural, a model where we 1:11:19.127 --> 1:11:21.102 can use neural networks. 1:11:21.102 --> 1:11:27.187 The nice thing is between everything together before we get some compliment. 1:11:27.187 --> 1:11:30.269 Each of them is trained independently. 1:11:30.210 --> 1:11:34.349 Which of course has a disadvantage that they might not best work together. 1:11:34.694 --> 1:11:36.601 Here everything is trained together. 1:11:36.601 --> 1:11:39.230 The continuous representation will look into that. 1:11:39.339 --> 1:11:41.846 That's very helpful soft. 1:11:41.846 --> 1:11:50.426 We then neonetworks are able to learn somehow the relation between words and that's very 1:11:50.426 --> 1:11:57.753 helpful because then we can more easily deal with words which didn't occur. 1:12:00.000 --> 1:12:05.240 One thing just to correlate that to interlingua based. 1:12:05.345 --> 1:12:07.646 So we have this as an actual language. 1:12:07.627 --> 1:12:11.705 And if you do an interlingual based approach but don't take an artificial. 1:12:11.731 --> 1:12:17.814 With no ambiguities, but with a natural language that's referred to as pivot based in tea and 1:12:17.814 --> 1:12:20.208 can be done with all the approaches. 1:12:20.208 --> 1:12:25.902 So the ideas instead of directly translating from German to French, you first translate 1:12:25.902 --> 1:12:29.073 from German to English and then from English to. 1:12:29.409 --> 1:12:40.954 French where the big advantage is that you might have a lot more data for these two directions 1:12:40.954 --> 1:12:43.384 than you have here. 1:12:44.864 --> 1:12:54.666 With this thank you and deserve more questions and a bit late I'm sorry and then I'll see 1:12:54.666 --> 1:12:55.864 you again.