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142 lines
6.7 KiB
Plaintext
142 lines
6.7 KiB
Plaintext
//- 💫 DOCS > API > ARCHITECTURE > NN MODEL ARCHITECTURE
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p
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| The parsing model is a blend of recent results. The two recent
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| inspirations have been the work of Eli Klipperwasser and Yoav Goldberg at
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| Bar Ilan#[+fn(1)], and the SyntaxNet team from Google. The foundation of
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| the parser is still based on the work of Joakim Nivre#[+fn(2)], who
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| introduced the transition-based framework#[+fn(3)], the arc-eager
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| transition system, and the imitation learning objective. The model is
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| implemented using #[+a(gh("thinc")) Thinc], spaCy's machine learning
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| library. We first predict context-sensitive vectors for each word in the
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| input:
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+code.
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(embed_lower | embed_prefix | embed_suffix | embed_shape)
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>> Maxout(token_width)
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>> convolution ** 4
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p
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| This convolutional layer is shared between the tagger, parser and NER,
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| and will also be shared by the future neural lemmatizer. Because the
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| parser shares these layers with the tagger, the parser does not require
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| tag features. I got this trick from David Weiss's "Stack Combination"
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| paper#[+fn(4)].
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p
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| To boost the representation, the tagger actually predicts a "super tag"
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| with POS, morphology and dependency label#[+fn(5)]. The tagger predicts
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| these supertags by adding a softmax layer onto the convolutional layer –
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| so, we're teaching the convolutional layer to give us a representation
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| that's one affine transform from this informative lexical information.
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| This is obviously good for the parser (which backprops to the
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| convolutions too). The parser model makes a state vector by concatenating
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| the vector representations for its context tokens. The current context
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| tokens:
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+table
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+row
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+cell #[code S0], #[code S1], #[code S2]
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+cell Top three words on the stack.
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+row
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+cell #[code B0], #[code B1]
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+cell First two words of the buffer.
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+row
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+cell.u-nowrap
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| #[code S0L1], #[code S1L1], #[code S2L1], #[code B0L1],
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| #[code B1L1]#[br]
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| #[code S0L2], #[code S1L2], #[code S2L2], #[code B0L2],
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| #[code B1L2]
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+cell
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| Leftmost and second leftmost children of #[code S0], #[code S1],
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| #[code S2], #[code B0] and #[code B1].
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+row
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+cell.u-nowrap
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| #[code S0R1], #[code S1R1], #[code S2R1], #[code B0R1],
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| #[code B1R1]#[br]
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| #[code S0R2], #[code S1R2], #[code S2R2], #[code B0R2],
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| #[code B1R2]
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+cell
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| Rightmost and second rightmost children of #[code S0], #[code S1],
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| #[code S2], #[code B0] and #[code B1].
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p
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| This makes the state vector quite long: #[code 13*T], where #[code T] is
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| the token vector width (128 is working well). Fortunately, there's a way
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| to structure the computation to save some expense (and make it more
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| GPU-friendly).
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p
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| The parser typically visits #[code 2*N] states for a sentence of length
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| #[code N] (although it may visit more, if it back-tracks with a
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| non-monotonic transition#[+fn(4)]). A naive implementation would require
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| #[code 2*N (B, 13*T) @ (13*T, H)] matrix multiplications for a batch of
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| size #[code B]. We can instead perform one #[code (B*N, T) @ (T, 13*H)]
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| multiplication, to pre-compute the hidden weights for each positional
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| feature with respect to the words in the batch. (Note that our token
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| vectors come from the CNN — so we can't play this trick over the
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| vocabulary. That's how Stanford's NN parser#[+fn(3)] works — and why its
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| model is so big.)
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p
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| This pre-computation strategy allows a nice compromise between
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| GPU-friendliness and implementation simplicity. The CNN and the wide
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| lower layer are computed on the GPU, and then the precomputed hidden
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| weights are moved to the CPU, before we start the transition-based
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| parsing process. This makes a lot of things much easier. We don't have to
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| worry about variable-length batch sizes, and we don't have to implement
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| the dynamic oracle in CUDA to train.
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p
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| Currently the parser's loss function is multilabel log loss#[+fn(6)], as
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| the dynamic oracle allows multiple states to be 0 cost. This is defined
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| as follows, where #[code gZ] is the sum of the scores assigned to gold
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| classes:
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+code.
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(exp(score) / Z) - (exp(score) / gZ)
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+bibliography
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+item
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| #[+a("https://www.semanticscholar.org/paper/Simple-and-Accurate-Dependency-Parsing-Using-Bidir-Kiperwasser-Goldberg/3cf31ecb2724b5088783d7c96a5fc0d5604cbf41") Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations]
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br
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| Eliyahu Kiperwasser, Yoav Goldberg. (2016)
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+item
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| #[+a("https://www.semanticscholar.org/paper/A-Dynamic-Oracle-for-Arc-Eager-Dependency-Parsing-Goldberg-Nivre/22697256ec19ecc3e14fcfc63624a44cf9c22df4") A Dynamic Oracle for Arc-Eager Dependency Parsing]
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br
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| Yoav Goldberg, Joakim Nivre (2012)
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+item
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| #[+a("https://explosion.ai/blog/parsing-english-in-python") Parsing English in 500 Lines of Python]
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br
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| Matthew Honnibal (2013)
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+item
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| #[+a("https://www.semanticscholar.org/paper/Stack-propagation-Improved-Representation-Learning-Zhang-Weiss/0c133f79b23e8c680891d2e49a66f0e3d37f1466") Stack-propagation: Improved Representation Learning for Syntax]
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br
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| Yuan Zhang, David Weiss (2016)
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+item
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| #[+a("https://www.semanticscholar.org/paper/Deep-multi-task-learning-with-low-level-tasks-supe-S%C3%B8gaard-Goldberg/03ad06583c9721855ccd82c3d969a01360218d86") Deep multi-task learning with low level tasks supervised at lower layers]
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br
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| Anders Søgaard, Yoav Goldberg (2016)
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+item
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| #[+a("https://www.semanticscholar.org/paper/An-Improved-Non-monotonic-Transition-System-for-De-Honnibal-Johnson/4094cee47ade13b77b5ab4d2e6cb9dd2b8a2917c") An Improved Non-monotonic Transition System for Dependency Parsing]
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br
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| Matthew Honnibal, Mark Johnson (2015)
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+item
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| #[+a("http://cs.stanford.edu/people/danqi/papers/emnlp2014.pdf") A Fast and Accurate Dependency Parser using Neural Networks]
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br
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| Danqi Cheng, Christopher D. Manning (2014)
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+item
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| #[+a("https://www.semanticscholar.org/paper/Parsing-the-Wall-Street-Journal-using-a-Lexical-Fu-Riezler-King/0ad07862a91cd59b7eb5de38267e47725a62b8b2") Parsing the Wall Street Journal using a Lexical-Functional Grammar and Discriminative Estimation Techniques]
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br
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| Stefan Riezler et al. (2002)
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