13,230 research outputs found
Teaching Machines to Read and Comprehend
Teaching machines to read natural language documents remains an elusive
challenge. Machine reading systems can be tested on their ability to answer
questions posed on the contents of documents that they have seen, but until now
large scale training and test datasets have been missing for this type of
evaluation. In this work we define a new methodology that resolves this
bottleneck and provides large scale supervised reading comprehension data. This
allows us to develop a class of attention based deep neural networks that learn
to read real documents and answer complex questions with minimal prior
knowledge of language structure.Comment: Appears in: Advances in Neural Information Processing Systems 28
(NIPS 2015). 14 pages, 13 figure
Copy mechanism and tailored training for character-based data-to-text generation
In the last few years, many different methods have been focusing on using
deep recurrent neural networks for natural language generation. The most widely
used sequence-to-sequence neural methods are word-based: as such, they need a
pre-processing step called delexicalization (conversely, relexicalization) to
deal with uncommon or unknown words. These forms of processing, however, give
rise to models that depend on the vocabulary used and are not completely
neural.
In this work, we present an end-to-end sequence-to-sequence model with
attention mechanism which reads and generates at a character level, no longer
requiring delexicalization, tokenization, nor even lowercasing. Moreover, since
characters constitute the common "building blocks" of every text, it also
allows a more general approach to text generation, enabling the possibility to
exploit transfer learning for training. These skills are obtained thanks to two
major features: (i) the possibility to alternate between the standard
generation mechanism and a copy one, which allows to directly copy input facts
to produce outputs, and (ii) the use of an original training pipeline that
further improves the quality of the generated texts.
We also introduce a new dataset called E2E+, designed to highlight the
copying capabilities of character-based models, that is a modified version of
the well-known E2E dataset used in the E2E Challenge. We tested our model
according to five broadly accepted metrics (including the widely used BLEU),
showing that it yields competitive performance with respect to both
character-based and word-based approaches.Comment: ECML-PKDD 2019 (Camera ready version
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