46 research outputs found
A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing
We present a novel neural network model that learns POS tagging and
graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to
learn feature representations shared for both POS tagging and dependency
parsing tasks, thus handling the feature-engineering problem. Our extensive
experiments, on 19 languages from the Universal Dependencies project, show that
our model outperforms the state-of-the-art neural network-based
Stack-propagation model for joint POS tagging and transition-based dependency
parsing, resulting in a new state of the art. Our code is open-source and
available together with pre-trained models at:
https://github.com/datquocnguyen/jPTDPComment: v2: also include universal POS tagging, UAS and LAS accuracies w.r.t
gold-standard segmentation on Universal Dependencies 2.0 - CoNLL 2017 shared
task test data; in CoNLL 201
An improved neural network model for joint POS tagging and dependency parsing
We propose a novel neural network model for joint part-of-speech (POS)
tagging and dependency parsing. Our model extends the well-known BIST
graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating
a BiLSTM-based tagging component to produce automatically predicted POS tags
for the parser. On the benchmark English Penn treebank, our model obtains
strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+%
absolute improvements to the BIST graph-based parser, and also obtaining a
state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental
results on parsing 61 "big" Universal Dependencies treebanks from raw texts
show that our model outperforms the baseline UDPipe (Straka and Strakov\'a,
2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS
score. In addition, with our model, we also obtain state-of-the-art downstream
task scores for biomedical event extraction and opinion analysis applications.
Our code is available together with all pre-trained models at:
https://github.com/datquocnguyen/jPTDPComment: 11 pages; In Proceedings of the CoNLL 2018 Shared Task: Multilingual
Parsing from Raw Text to Universal Dependencies, to appea
A neural network architecture for detecting grammatical errors in statistical machine translation
In this paper we present a Neural Network (NN) architecture for detecting grammatical er- rors in Statistical Machine Translation (SMT) using monolingual morpho-syntactic word rep- resentations in combination with surface and syntactic context windows. We test our approach on two language pairs and two tasks, namely detecting grammatical errors and predicting over- all post-editing e ort. Our results show that this approach is not only able to accurately detect grammatical errors but it also performs well as a quality estimation system for predicting over- all post-editing e ort, which is characterised by all types of MT errors. Furthermore, we show that this approach is portable to other languages