10,338 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
RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition
We compare the fast training and decoding speed of RETURNN of attention
models for translation, due to fast CUDA LSTM kernels, and a fast pure
TensorFlow beam search decoder. We show that a layer-wise pretraining scheme
for recurrent attention models gives over 1% BLEU improvement absolute and it
allows to train deeper recurrent encoder networks. Promising preliminary
results on max. expected BLEU training are presented. We are able to train
state-of-the-art models for translation and end-to-end models for speech
recognition and show results on WMT 2017 and Switchboard. The flexibility of
RETURNN allows a fast research feedback loop to experiment with alternative
architectures, and its generality allows to use it on a wide range of
applications.Comment: accepted as demo paper on ACL 201
RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data Augmentation
We present state-of-the-art automatic speech recognition (ASR) systems
employing a standard hybrid DNN/HMM architecture compared to an attention-based
encoder-decoder design for the LibriSpeech task. Detailed descriptions of the
system development, including model design, pretraining schemes, training
schedules, and optimization approaches are provided for both system
architectures. Both hybrid DNN/HMM and attention-based systems employ
bi-directional LSTMs for acoustic modeling/encoding. For language modeling, we
employ both LSTM and Transformer based architectures. All our systems are built
using RWTHs open-source toolkits RASR and RETURNN. To the best knowledge of the
authors, the results obtained when training on the full LibriSpeech training
set, are the best published currently, both for the hybrid DNN/HMM and the
attention-based systems. Our single hybrid system even outperforms previous
results obtained from combining eight single systems. Our comparison shows that
on the LibriSpeech 960h task, the hybrid DNN/HMM system outperforms the
attention-based system by 15% relative on the clean and 40% relative on the
other test sets in terms of word error rate. Moreover, experiments on a reduced
100h-subset of the LibriSpeech training corpus even show a more pronounced
margin between the hybrid DNN/HMM and attention-based architectures.Comment: Proceedings of INTERSPEECH 201
Sequence to Sequence Mixture Model for Diverse Machine Translation
Sequence to sequence (SEQ2SEQ) models often lack diversity in their generated
translations. This can be attributed to the limitation of SEQ2SEQ models in
capturing lexical and syntactic variations in a parallel corpus resulting from
different styles, genres, topics, or ambiguity of the translation process. In
this paper, we develop a novel sequence to sequence mixture (S2SMIX) model that
improves both translation diversity and quality by adopting a committee of
specialized translation models rather than a single translation model. Each
mixture component selects its own training dataset via optimization of the
marginal loglikelihood, which leads to a soft clustering of the parallel
corpus. Experiments on four language pairs demonstrate the superiority of our
mixture model compared to a SEQ2SEQ baseline with standard or diversity-boosted
beam search. Our mixture model uses negligible additional parameters and incurs
no extra computation cost during decoding.Comment: 11 pages, 5 figures, accepted to CoNLL201
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