5,650 research outputs found
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
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
In this paper, we propose a novel neural network model called RNN
Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN
encodes a sequence of symbols into a fixed-length vector representation, and
the other decodes the representation into another sequence of symbols. The
encoder and decoder of the proposed model are jointly trained to maximize the
conditional probability of a target sequence given a source sequence. The
performance of a statistical machine translation system is empirically found to
improve by using the conditional probabilities of phrase pairs computed by the
RNN Encoder-Decoder as an additional feature in the existing log-linear model.
Qualitatively, we show that the proposed model learns a semantically and
syntactically meaningful representation of linguistic phrases.Comment: EMNLP 201
Guided Open Vocabulary Image Captioning with Constrained Beam Search
Existing image captioning models do not generalize well to out-of-domain
images containing novel scenes or objects. This limitation severely hinders the
use of these models in real world applications dealing with images in the wild.
We address this problem using a flexible approach that enables existing deep
captioning architectures to take advantage of image taggers at test time,
without re-training. Our method uses constrained beam search to force the
inclusion of selected tag words in the output, and fixed, pretrained word
embeddings to facilitate vocabulary expansion to previously unseen tag words.
Using this approach we achieve state of the art results for out-of-domain
captioning on MSCOCO (and improved results for in-domain captioning). Perhaps
surprisingly, our results significantly outperform approaches that incorporate
the same tag predictions into the learning algorithm. We also show that we can
significantly improve the quality of generated ImageNet captions by leveraging
ground-truth labels.Comment: EMNLP 201
Statistical Machine Translation Features with Multitask Tensor Networks
We present a three-pronged approach to improving Statistical Machine
Translation (SMT), building on recent success in the application of neural
networks to SMT. First, we propose new features based on neural networks to
model various non-local translation phenomena. Second, we augment the
architecture of the neural network with tensor layers that capture important
higher-order interaction among the network units. Third, we apply multitask
learning to estimate the neural network parameters jointly. Each of our
proposed methods results in significant improvements that are complementary.
The overall improvement is +2.7 and +1.8 BLEU points for Arabic-English and
Chinese-English translation over a state-of-the-art system that already
includes neural network features.Comment: 11 pages (9 content + 2 references), 2 figures, accepted to ACL 2015
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