11,086 research outputs found
Learning to Parse and Translate Improves Neural Machine Translation
There has been relatively little attention to incorporating linguistic prior
to neural machine translation. Much of the previous work was further
constrained to considering linguistic prior on the source side. In this paper,
we propose a hybrid model, called NMT+RNNG, that learns to parse and translate
by combining the recurrent neural network grammar into the attention-based
neural machine translation. Our approach encourages the neural machine
translation model to incorporate linguistic prior during training, and lets it
translate on its own afterward. Extensive experiments with four language pairs
show the effectiveness of the proposed NMT+RNNG.Comment: Accepted as a short paper at the 55th Annual Meeting of the
Association for Computational Linguistics (ACL 2017
A Nested Attention Neural Hybrid Model for Grammatical Error Correction
Grammatical error correction (GEC) systems strive to correct both global
errors in word order and usage, and local errors in spelling and inflection.
Further developing upon recent work on neural machine translation, we propose a
new hybrid neural model with nested attention layers for GEC. Experiments show
that the new model can effectively correct errors of both types by
incorporating word and character-level information,and that the model
significantly outperforms previous neural models for GEC as measured on the
standard CoNLL-14 benchmark dataset. Further analysis also shows that the
superiority of the proposed model can be largely attributed to the use of the
nested attention mechanism, which has proven particularly effective in
correcting local errors that involve small edits in orthography
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
Neural Responding Machine for Short-Text Conversation
We propose Neural Responding Machine (NRM), a neural network-based response
generator for Short-Text Conversation. NRM takes the general encoder-decoder
framework: it formalizes the generation of response as a decoding process based
on the latent representation of the input text, while both encoding and
decoding are realized with recurrent neural networks (RNN). The NRM is trained
with a large amount of one-round conversation data collected from a
microblogging service. Empirical study shows that NRM can generate
grammatically correct and content-wise appropriate responses to over 75% of the
input text, outperforming state-of-the-arts in the same setting, including
retrieval-based and SMT-based models.Comment: accepted as a full paper at ACL 201
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
Most recent approaches use the sequence-to-sequence model for paraphrase
generation. The existing sequence-to-sequence model tends to memorize the words
and the patterns in the training dataset instead of learning the meaning of the
words. Therefore, the generated sentences are often grammatically correct but
semantically improper. In this work, we introduce a novel model based on the
encoder-decoder framework, called Word Embedding Attention Network (WEAN). Our
proposed model generates the words by querying distributed word representations
(i.e. neural word embeddings), hoping to capturing the meaning of the according
words. Following previous work, we evaluate our model on two
paraphrase-oriented tasks, namely text simplification and short text
abstractive summarization. Experimental results show that our model outperforms
the sequence-to-sequence baseline by the BLEU score of 6.3 and 5.5 on two
English text simplification datasets, and the ROUGE-2 F1 score of 5.7 on a
Chinese summarization dataset. Moreover, our model achieves state-of-the-art
performances on these three benchmark datasets.Comment: arXiv admin note: text overlap with arXiv:1710.0231
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