13,531 research outputs found
Neural Machine Translation with Word Predictions
In the encoder-decoder architecture for neural machine translation (NMT), the
hidden states of the recurrent structures in the encoder and decoder carry the
crucial information about the sentence.These vectors are generated by
parameters which are updated by back-propagation of translation errors through
time. We argue that propagating errors through the end-to-end recurrent
structures are not a direct way of control the hidden vectors. In this paper,
we propose to use word predictions as a mechanism for direct supervision. More
specifically, we require these vectors to be able to predict the vocabulary in
target sentence. Our simple mechanism ensures better representations in the
encoder and decoder without using any extra data or annotation. It is also
helpful in reducing the target side vocabulary and improving the decoding
efficiency. Experiments on Chinese-English and German-English machine
translation tasks show BLEU improvements by 4.53 and 1.3, respectivelyComment: Accepted at EMNLP201
Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding
Learning long-term dependencies in extended temporal sequences requires
credit assignment to events far back in the past. The most common method for
training recurrent neural networks, back-propagation through time (BPTT),
requires credit information to be propagated backwards through every single
step of the forward computation, potentially over thousands or millions of time
steps. This becomes computationally expensive or even infeasible when used with
long sequences. Importantly, biological brains are unlikely to perform such
detailed reverse replay over very long sequences of internal states (consider
days, months, or years.) However, humans are often reminded of past memories or
mental states which are associated with the current mental state. We consider
the hypothesis that such memory associations between past and present could be
used for credit assignment through arbitrarily long sequences, propagating the
credit assigned to the current state to the associated past state. Based on
this principle, we study a novel algorithm which only back-propagates through a
few of these temporal skip connections, realized by a learned attention
mechanism that associates current states with relevant past states. We
demonstrate in experiments that our method matches or outperforms regular BPTT
and truncated BPTT in tasks involving particularly long-term dependencies, but
without requiring the biologically implausible backward replay through the
whole history of states. Additionally, we demonstrate that the proposed method
transfers to longer sequences significantly better than LSTMs trained with BPTT
and LSTMs trained with full self-attention.Comment: To appear as a Spotlight presentation at NIPS 201
Probabilistic Programming Concepts
A multitude of different probabilistic programming languages exists today,
all extending a traditional programming language with primitives to support
modeling of complex, structured probability distributions. Each of these
languages employs its own probabilistic primitives, and comes with a particular
syntax, semantics and inference procedure. This makes it hard to understand the
underlying programming concepts and appreciate the differences between the
different languages. To obtain a better understanding of probabilistic
programming, we identify a number of core programming concepts underlying the
primitives used by various probabilistic languages, discuss the execution
mechanisms that they require and use these to position state-of-the-art
probabilistic languages and their implementation. While doing so, we focus on
probabilistic extensions of logic programming languages such as Prolog, which
have been developed since more than 20 years
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