301 research outputs found
A Neural Attention Model for Abstractive Sentence Summarization
Summarization based on text extraction is inherently limited, but
generation-style abstractive methods have proven challenging to build. In this
work, we propose a fully data-driven approach to abstractive sentence
summarization. Our method utilizes a local attention-based model that generates
each word of the summary conditioned on the input sentence. While the model is
structurally simple, it can easily be trained end-to-end and scales to a large
amount of training data. The model shows significant performance gains on the
DUC-2004 shared task compared with several strong baselines.Comment: Proceedings of EMNLP 201
Adapting Sequence Models for Sentence Correction
In a controlled experiment of sequence-to-sequence approaches for the task of
sentence correction, we find that character-based models are generally more
effective than word-based models and models that encode subword information via
convolutions, and that modeling the output data as a series of diffs improves
effectiveness over standard approaches. Our strongest sequence-to-sequence
model improves over our strongest phrase-based statistical machine translation
model, with access to the same data, by 6 M2 (0.5 GLEU) points. Additionally,
in the data environment of the standard CoNLL-2014 setup, we demonstrate that
modeling (and tuning against) diffs yields similar or better M2 scores with
simpler models and/or significantly less data than previous
sequence-to-sequence approaches.Comment: EMNLP 201
Character-Aware Neural Language Models
We describe a simple neural language model that relies only on
character-level inputs. Predictions are still made at the word-level. Our model
employs a convolutional neural network (CNN) and a highway network over
characters, whose output is given to a long short-term memory (LSTM) recurrent
neural network language model (RNN-LM). On the English Penn Treebank the model
is on par with the existing state-of-the-art despite having 60% fewer
parameters. On languages with rich morphology (Arabic, Czech, French, German,
Spanish, Russian), the model outperforms word-level/morpheme-level LSTM
baselines, again with fewer parameters. The results suggest that on many
languages, character inputs are sufficient for language modeling. Analysis of
word representations obtained from the character composition part of the model
reveals that the model is able to encode, from characters only, both semantic
and orthographic information.Comment: AAAI 201
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