2,731 research outputs found
Neural Word Segmentation with Rich Pretraining
Neural word segmentation research has benefited from large-scale raw texts by
leveraging them for pretraining character and word embeddings. On the other
hand, statistical segmentation research has exploited richer sources of
external information, such as punctuation, automatic segmentation and POS. We
investigate the effectiveness of a range of external training sources for
neural word segmentation by building a modular segmentation model, pretraining
the most important submodule using rich external sources. Results show that
such pretraining significantly improves the model, leading to accuracies
competitive to the best methods on six benchmarks.Comment: Accepted by ACL 201
Universal Dependencies Parsing for Colloquial Singaporean English
Singlish can be interesting to the ACL community both linguistically as a
major creole based on English, and computationally for information extraction
and sentiment analysis of regional social media. We investigate dependency
parsing of Singlish by constructing a dependency treebank under the Universal
Dependencies scheme, and then training a neural network model by integrating
English syntactic knowledge into a state-of-the-art parser trained on the
Singlish treebank. Results show that English knowledge can lead to 25% relative
error reduction, resulting in a parser of 84.47% accuracies. To the best of our
knowledge, we are the first to use neural stacking to improve cross-lingual
dependency parsing on low-resource languages. We make both our annotation and
parser available for further research.Comment: Accepted by ACL 201
Data-driven Natural Language Generation: Paving the Road to Success
We argue that there are currently two major bottlenecks to the commercial use
of statistical machine learning approaches for natural language generation
(NLG): (a) The lack of reliable automatic evaluation metrics for NLG, and (b)
The scarcity of high quality in-domain corpora. We address the first problem by
thoroughly analysing current evaluation metrics and motivating the need for a
new, more reliable metric. The second problem is addressed by presenting a
novel framework for developing and evaluating a high quality corpus for NLG
training.Comment: WiNLP workshop at ACL 201
BLEU is Not Suitable for the Evaluation of Text Simplification
BLEU is widely considered to be an informative metric for text-to-text
generation, including Text Simplification (TS). TS includes both lexical and
structural aspects. In this paper we show that BLEU is not suitable for the
evaluation of sentence splitting, the major structural simplification
operation. We manually compiled a sentence splitting gold standard corpus
containing multiple structural paraphrases, and performed a correlation
analysis with human judgments. We find low or no correlation between BLEU and
the grammaticality and meaning preservation parameters where sentence splitting
is involved. Moreover, BLEU often negatively correlates with simplicity,
essentially penalizing simpler sentences.Comment: Accepted to EMNLP 2018 (Short papers
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