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the language understanding group here it idea um
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this is studying a natural language understanding which in my opinion
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is is the greatest challenge in a high because
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essentially anything you can think are almost anything you can think
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can be said in natural language and you can understand
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uh that natural language so it essentially me is a form of of mind reading
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we can read each other's minds we can communicate thoughts directly into
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another person's head just by making sounds that's incredible um
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but there are the kinds of things that we don't know we don't understand about this process
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in particular things like what is the structure of human
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thought if if we can can really communicate any
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any idea in language then that tells a lot about what ideas were capable
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of having 'cause those are the things that can be expressed in language
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how the how do we understand language how can we calculate
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this thought from just hearing a a sequence of sounds
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how can we decide if i make this sequence of sounds and
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that person is going to have this thought in their head
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um and my arguments you only animals i can do this in their other animal certainly do communicate
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but not with anything like this structure uh and complexity
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of structure that humans are able to do
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so is that because uh animals i don't have the there's some you
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know innate human ability to to speak in there from by
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communicate our hearts more effectively or is it just that that thoughts themselves are much simpler
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we have more complicated thoughts and that's why we make more complicated uh communications
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we don't know that
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this so this is the fastening problem for a i um
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in our group we're looking at uh as a understanding natural language through a
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natural language processing n. l. p. and the focusing on representation learning so
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uh we work on deep neural network architectures for representation learning applied
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to multiple different tasks um in particular focusing on machine translation
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information access in classification and a semantic entanglement
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uh this uh maybe the head a new because
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others to post on joan leslie r. p. h.
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d. students we also work closely with andrei
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the best to police who was the head of the n. l. p. group before i arrived
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uh we work closely with people in the speech and audio processing group
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always shared project projects and i've collaborations homer low in geneva
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and jenna pop up used to student uh and my from for their former employer
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um so our current research topics are
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as i said representation learning that
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is pervasive machine translation information access in classification and actual entanglement
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made a number of 'em contributions in this area uh we sites
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and a lot of places where we've published this work
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um
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i won't go into the whole list but i will uh
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go into more detail for some of these other contributions
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so just as an overview of work on representation learning
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um
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we've done a lot of work that's related to attention based representations
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so attention over the different uh meanings that are
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word could have attention over the previous um
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words in the decoder when you're generating a sequence of words
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a hierarchical attention that will uh describe in this moment
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and uh a bag of factor i'm betting was uh the non parametric embedded graphs
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um output representation learning uh these are
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ways to general lies to um
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new outputs by taking advantage of the descriptions of those outputs
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uh which will talk in particular about the label law where text classification and um
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i work on internment factors representing intel meant in a vector space
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uh for uh and semantics of words and looking
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at a compositional semantics so we'll talk
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a bit about how to play those representations a tech show and tell me
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so for some machine translation
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i just two pieces of work the sense of where
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um neural machine translation and it's uh the
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self attentive residual decoder for no machine translation
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so neural machine translation works by or uh
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running a neural network over the input sentence computing a a representation
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and then conditioning on our representation to generate the
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sentence uh for the output the target language
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um
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one of the problems is that uh for the input sentence
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you often have words that are ambiguous him or
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at least in the target language there are more than one ways in which you could translate them
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so if you can decision on and big us a
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workers of representations of words that are ambiguous
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you quite quickly get into problems because of the the company towards of all these ambiguities
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also you end up with a models that are very biased words
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most frequent senses which might not apply for given case uh_huh
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um so the solution is to explicitly add a word since the
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us in big you asian component to the in coder
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uh you first look at your input text decide
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on what sense each we're word uh
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should have uh at least the nouns and verbs which are the ambiguous ones
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and then condition on those senses of when you're
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compute your representation of the input a sentence
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um
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so that's one piece of work the second one is once you've conditioned on
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with the the representation of the input sentence you need to generate
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the translation of that sentence as your output sentence
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and this uh if you're doing it with the recurrent neural network as a problem that
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it's very biased uh the the recurrent neural network one is
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trying to predict the next word it should generate
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very biased towards uh looking only at the previous few words that it's already generated
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when in fact lots of previous words or previous words that are far away maybe very relevant
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so the idea is to add an attention mechanism there so that you can decide
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uh which of the previous words you need to take pay
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attention to to generate if the next word um
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and in particular this is an attention mechanism that uh
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looks at the specific words that's what it's called the residual connection
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rather than looking at uh the the hidden representations and the for previous steps
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um and that improves um improves the quality of the translation
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output and if you look at those attention patterns
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you see something that looks very much like a syntax syntactic structure that
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it's learning model of how to generate the output and that model is capturing
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some information about the syntax of the the sentence that is generating
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in areas in indexing and and
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classifications so information access problems
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um
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uh when the problem is that um we need to build the
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do with multiple languages for for solving these tasks um
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and often we do not have a lot enough data for all
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our languages so we need to be other somehow share
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parameters share information learned about one language to another language
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and here the solution is to have a hierarchical attention mechanism
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the first layer here uh uh for each individual sentence decides which word to look
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at which words to focus on when computing the representation of that sentence
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the second level says uh which sentences should really focus
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on when computing the representation of the entire document
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for doing our uh retrieval or categorisation of documents
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and the uh multilingual part has to do with which parts of this model
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you share do you share these these representations that ever there
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will be in the same encoded in the same why mapping from words to encoding
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or you share these attention mechanisms that uh when
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once you've got the uh to uh encoding
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at one level how do you apply attention to compute the coding at the next level
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so you might wanna change a shared a the encoding you might wanna
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share the uh attention or you might want to share both
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and so uh we've done experiments on on what's the appropriate
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level of of sharing for um multilingual text classification
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um another piece of work in this area is label where i'm a
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decoder for classification for for deciding which class something is is in
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here you're in a setting where the classes have labels so there is a piece of tax
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telling you what you're out what should be a um you know
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how the a piece of text describing want you to each of your possible output you're allowed to choose
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so these tracts getting crowded in the same way using the same wording beheadings of
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factors that represent words as the input tax that you're trying to classify
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then you computed joint space between these two representations and
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then train a classifier and that joint space
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um and that leverage is the semantics of your labels themselves which is a
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very useful in many cases especially if you have a very large number
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of classes or you have two classes that you have no training data
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for but you have a label you can still uses method
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so this uh particular uh a solution approach
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to solving this problem actually not only
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uh handles and seen labels are rare labels it even improves
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on the places where you do have have enough data uh
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i'm not there other thing we're working on is textual entail men
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so actually tell meant is information inclusion so you want to
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be able to say this statement about health care
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is a includes the information uh this simpler more abstract
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piece of information so it's also kind of abstraction
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this is an abstraction of both these two different state means even though they're different
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it's a fundamental concept actual entertainment for for this man takes of language
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and as you can imagine it's useful for doing opinion summarisation
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if you have a very diverse everybody's opinions is different
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but maybe you can abstract out some consensus opinions
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that large numbers of people agree on and summarise based on that
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so what we've done um is develop
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models of of actual entitlement
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that are based on two ideas uh first of all to deal with this um
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to deal with this problem that the
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the the sentences are can be very long and so you want to say that
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uh if you want to say the information in the be entailed part
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is a subset of the information in the in telling part
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it may just be that it's a subset of the words or a subset of the the semantic entities
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uh in the two representations so we have um
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a bag of factor which representations for each part and then uh
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we assume that there's for every vector in the entailed
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bag uh there exists at least one vector need tailing bag
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uh oh such that one the vectors entailed each other
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and that but that leaves open the question of how do you represent entanglement between vectors
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so we have a whole line of work on how to model
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entailed in a in a vector space cancelled answer that question
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uh right so this is what i just said every vector
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in the entailed sense has to be entailed by some factor
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in in telling sentence and uh we have of
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a theoretical framework for measuring entanglement in a in a vector space
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so that framework uh lets us um represent
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propositions that can be known about uh in
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about something i'm i'm
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as bits in the vector and then uh there's a
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prior that captures the constraints over the whole factor
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the framework tells us a way to measure the intel meant to
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be given to vectors and how to for a vector
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a given other in vectors that it entails or is until right
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we use this framework to define a a distribution semantic model
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so distributional semantics says that all meaning of a word is um you can
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tell the meaning of a word by looking at the distribution of words
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that that word coworkers with in texas just by looking at very large
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amounts of text uh you can infer the meaning of a word
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we propose this uh to internet based model of
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of why the meaning of a word tells
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you something about the the meaning of the other words that are in the same context
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um by assuming there's some pseudo phrase that that a
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unifying these two vectors together gives you some coherent
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uh as a semantics uh that so it reflects the sense in which these
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two words are played both playing a role in some coherent larger semantics
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so by uh representing bad in entertainment framework
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uh we can train models that gives us state of
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the art uh results on predicting hype on me
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so does uh is the word catch a
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a hypo name of the word animal
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so that's just an overview of us and the things we're working on working
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on understanding natural language to an l. p. and representation learning um
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looking at people earning architectures for doing that representation learning
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machine translation and um information access
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and semantic internment problems

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Presentation of the «Natural Language Understanding» research group
HENDERSON, James, Idiap Senior Researcher
Aug. 29, 2018 · 2:03 p.m.
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