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Natural Language Processing Language Modelling and Machine Translation 深度学习暑期学校2017

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Natural Language Processing, Language

Modelling and Machine Translation

Phil Blunsom

in collaboration with the

DeepMind Natural Language Group

pblunsom@google.com

Natural Language Processing

Linguistics

Why are human languages the way that they are?

How does the brain map from raw linguistic input to

meaning and back again? And how do children learn

language so quickly?

Computational Linguistics

Computational models of language and

computational tools for studying language.

Natural Language Processing

Building tools for processing language and

applications that use language:

•

Intrinsic: Parsing, Language Modelling, etc.

•

Extrinsic: ASR, MT, QA/Dialogue, etc.

Language models

A language model assigns a probability to a sequence of words,

such that

w∈Σ

∗

p(w) = 1:

Given the observed training text, how probable is this new

utterance?

Thus we can compare diﬀerent orderings of words

(e.g. Translation):

p(he likes apples) > p(apples likes he)

or choice of words (e.g. Speech Recognition):

p(he likes apples) > p(he licks apples)

History: cryptography

Language models

Much of Natural Language Processing can be structured as

(conditional) language modelling:

Translation

(Les chiens aiment les os ||| Dogs love bones)

Question Answering

(What do dogs love? ||| bones . |β)

Dialogue

(How are you? ||| Fine thanks. And you? |β)

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