11,137 research outputs found
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
Table-to-Text: Describing Table Region with Natural Language
In this paper, we present a generative model to generate a natural language
sentence describing a table region, e.g., a row. The model maps a row from a
table to a continuous vector and then generates a natural language sentence by
leveraging the semantics of a table. To deal with rare words appearing in a
table, we develop a flexible copying mechanism that selectively replicates
contents from the table in the output sequence. Extensive experiments
demonstrate the accuracy of the model and the power of the copying mechanism.
On two synthetic datasets, WIKIBIO and SIMPLEQUESTIONS, our model improves the
current state-of-the-art BLEU-4 score from 34.70 to 40.26 and from 33.32 to
39.12, respectively. Furthermore, we introduce an open-domain dataset
WIKITABLETEXT including 13,318 explanatory sentences for 4,962 tables. Our
model achieves a BLEU-4 score of 38.23, which outperforms template based and
language model based approaches.Comment: 9 pages, 4 figures. This paper has been published by AAAI201
Generating Synthetic Data for Neural Keyword-to-Question Models
Search typically relies on keyword queries, but these are often semantically
ambiguous. We propose to overcome this by offering users natural language
questions, based on their keyword queries, to disambiguate their intent. This
keyword-to-question task may be addressed using neural machine translation
techniques. Neural translation models, however, require massive amounts of
training data (keyword-question pairs), which is unavailable for this task. The
main idea of this paper is to generate large amounts of synthetic training data
from a small seed set of hand-labeled keyword-question pairs. Since natural
language questions are available in large quantities, we develop models to
automatically generate the corresponding keyword queries. Further, we introduce
various filtering mechanisms to ensure that synthetic training data is of high
quality. We demonstrate the feasibility of our approach using both automatic
and manual evaluation. This is an extended version of the article published
with the same title in the Proceedings of ICTIR'18.Comment: Extended version of ICTIR'18 full paper, 11 page
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