 More or less, semantically, we can't. But my idea for it is a structure and the theory of it. So the two sentences that you see here, that they have been more minister of want of something and they are as common as Turkish imperialism and religious professionalism, they mean the same. As human, you can see that they are a character of each other. Is this a textual similarity problem? Yes, I mean, two paradoxes have to be actually similar. Otherwise, they are very unlikely to be characters. But I think it's not necessarily obvious the case because, for example, the parallelization is the kind of a binary graph that we're interested in, the parallelization of now. The similarity is essentially measured by the name of the scale and also the extent of the value. Also, parallelism, if you can define, for example, the minus of the square root of G, extensively used in this line of research, is always defined as a misunderstanding. So it's similarity can be defined as any level of it. The other very commonly used in popular class is because textual in daily information. So the way it is defined is that even two text flags, one is for the text and one is for the hypothesis, you would like to say that the D and base H gives the reading G, we can infer whether H is writing with two of them. Right? So it also says that D implies H. What you see here is that E is the post-mortem. First of the three lines of the text, we have three hypothesis listed. Let's think H1. H1 says that E might have called an American company. If you read the article, then it says that Huston based company, Lexxor, by DMI, it was called Chess. Now we know what chess and acquisition is really affecting. We know Huston is an American. From all this knowledge, we can actually say that there's not that much of a difference. Similarly, you can see that the semi-hypothex is also true, but it probably is not. Because on another, we can infer from the text. Maybe through that lemma, it is an empire-owned concern, but it is not accurate from the whatever nature of the text. Actually, we cannot say. Again, these two pieces of text are textual hypothesis, has to be related or similar, if they have to be in their own, or if the E has to be in their cage, but if they make it is not necessarily and other big differences is that extra entailment is upon completion. So, T plus H, but that doesn't as well. Obviously, we need to use it to achieve attribution, whether it is particular or particular. Unimplementation, I think, is a problem of blocking some text and not giving the on-chip attribution to own site of a particular paper. So, two pieces of text have to be similar to be considered for completionism. So, they are, this is an indicator, but not, it is not a case of this. So, essentially, the emphasis is on the unauthorized user's unacknowledged blocking of the top. So, we used to be like this indicator, but it is not necessarily about text normalization. Similarly, when we want to do it at different job sites, which are used in a particular order, we put one on set, we come in relation to the subject. We want to kind of have each of these things up, we all know, which essentially measures that how many, how many system-directed translation has, with the human-provided reference, and the reference translation is that the office serves the responsible for airport security. So, there are certainly more challenges, because the reference translations in this case may not be one. Yeah, if you want to go to translation, five of us will put the same sentence for Marathi in English. It will be, it will be different for all of them, right? So, there are actually, the similarity between two pieces of text is essentially the enamel of the board, and the foster of the object. So, text of similar text has been used in these cases, as well as in the content that is similar. Now, content that was exactly and the two pieces of text look similar in terms of their composition of the boards and panels, right? So, that's what we are calling as lexical. We will see some examples with the rectifier, but then in being the same, the main loop-taper and combination of being the same, between the same, but they are the same one. Now, this kind of text, in terms of how to build this system, they were more resources to more contextual author to actually build this system. We can go and see some examples. Similarity is of the granularity of the text. So, we talk about text book. So, what are the some things? But when we go from the basic idea is that if you have an instrument that is in the house, which you don't get with and we'll see some examples of how to build the object, right? But basically, what it says is that how the names of your names or enterprises or instances are built. This is very interesting, it's shadowed, which is true, but it was mentioned in many, many, in the case we are talking about the elastic element. Right, exactly. The processions have to be similar, same to get another process done. If it's not enough, even if it's in the elastic element. So, we look into most elastic elements. Human clear yet, which has been developed by the Universal Universal Universe for many, many years by several human experts, when we're seeing the same or the one of the same kind and the rich linguistic knowledge, then you can actually, you take that and measure this similar to any two words, right? How to be two words, right? The most common ancestor of two words in that, let's call it a scheme, right? And it's also important in the most popular example. But of course, we can have nationally, we can have lists and resources, kind of, yes, of course there are both the code style, the new one style, new domain, the vector space, the presentation of text, or those which, right? And the same notion of similarity would be defined here which captures that which term's operating, which document, and then we use these operations on the overall value of the composition and to obtain a space where, again, the basic idea of a similar term should come close. But here, by the way, the main standard for many, many years in this space would be, in fact, the simple applications of distance, dimensions, such as in 3D and of course, the basis of the distribution of the record is probably three years old and it essentially says that the words that are offered in a similar context to have similar meanings has in mind as being leveled by and by the large computing capacity that we have which defines very, very safety factors, what would be, I guess, the problem with the most popular vector space model which, in turn, frame a large amount of data and show that it's not any documentous insight that the vector space model is the right standard. Now, this is for any item in this process, vector space model, but there are more vectors at the end which are which we call it. Now, most of these techniques do not require any very simple investment. They take these kinds of techniques and the models which are three range and use them in our applications. So, there are several open-source and compacts which are available today in the project which you can just go and use this technique already or even these models about the care and also you can just very easily use. So, many types of start-up, by one to four first thing you should try is working before kind of really complicated model and it's still used in our full-source question because we just won't have the ability to automatically read the second one is this is a two-piece complex in terms of similarity because the students can't make up a bit more than the model also and in that case, we don't want to be looking into that model while in some of the confidential data set confidential when students are answering they help me because for example when one is this in this that is to say the basic similarities in the ego that do not that is the basic that's in general like supervisor can apply in many cases build so you don't you call over that it shouldn't much better so that we just need to figure out the two different types of similarity this first set of presentation which gives you the best view which help you to preserve in another case people have on the level of this job fixed length vector so we have ran everything essentially with right so that we have defined this border similarities that you can find which correspond to each other and then find similarities which part of important in deciding that other business enterprises which is a way of space to arrive at resources when you have a lot of many data people are great you can bring them complicated for example in the first instance we have write like a matrix for example in the case of let's say a emoji where are you looking at certain tags for all those complex derived embeddings right so let's say what could where patients or extensions are going to pose on these very popular people who are talking that you don't share a basic document as that you can use a document which is called a sequence learning model in the cases if you have orders take this code and write it down the reason what we have standard building blocks and just take this and apply on it and see how they are doing but if you want to build this quite it's quite it's quite though then you take and you and then you take and you can you can rest and rest and rest You have one more answer. We have thousands of answers. Right from the heart, similar to the question that we get from the second one. But comparing that, it is supposed to be 30. Compare that to what we were doing. Two left from all the pairs, I mean two left from all the sprinkles. But they are in the same quantity. There is only one more answer, 20 sprinkles also. You do this for 20 sprinkles. Right, now you have learned a lot. You have just opened the sprinkles. For which one would be ready to go. Because these have not been understood. Now you can use this particular method. To get to the bottom of the sprinkles. That is a very good question. That is what I can get. Where I have shown that if the model answer changes, then the performance of this technique is definitely better. Practical problem. Because otherwise, if you are out there always, especially against one more answer, it is the performance of this system. If it is something that is not straight line, if it is high level, it can be something everywhere. For example, what I mentioned earlier, 5 seconds now, if it is low level. If the model answer changes, then we can assume that they will not be able to do it. Because it is an answer that can be provided after some time. So, what percentage, that amount right above, then they will be able to see the match. So, what do you mean by that? So, what do you mean by that? So, what do you mean by that? Any way you can. Okay, first thing, the grammatical character is something that we do not need to imagine. In this case, there is a grammatical character. Boy, boy. And now, I am not very sure, we have tried that. I am not very sure that I am going to answer. For example, here, the emotion of this, how over the function, because it is like a byte. The model answer is, where the function changes. This guy is very hard to understand. Looks at the number, it is the function. Now, what is it? Some of us have been able to understand this universe, where it was, in certain copies, I don't want to know if there is a difference in each pair, but in other ways, there will be a difference. So, how much time does it need to get ready for the variation that we are going to have in the game? I would say, much. So, my own view here was to look at this scenario for this kind of activity. That means I am saying that I am very specific in this case that I am talking about, even at this level, I am not emotional at all because it is so interesting. That should be done. I am talking much in cases. For example, in a padlock, I am thinking that, in your social rights today, because there are many people who support that, you can't have thinking models transferring from your subjects, on subjects of liberty.