8,010 research outputs found
UGENT-LT3 SCATE system for machine translation quality estimation
This paper describes the submission of the UGENT-LT3 SCATE system to the WMT15 Shared Task on Quality Estima-tion (QE), viz. English-Spanish word and sentence-level QE. We conceived QE as a supervised Machine Learning (ML) problem and designed additional features and combined these with the baseline feature set to estimate quality. The sen-tence-level QE system re-uses the word level predictions of the word-level QE system. We experimented with different learning methods and observe improve-ments over the baseline system for word-level QE with the use of the new features and by combining learning methods into ensembles. For sentence-level QE we show that using a single feature based on word-level predictions can perform better than the baseline system and using this in combination with additional features led to further improvements in performance
Authorship Attribution Using a Neural Network Language Model
In practice, training language models for individual authors is often
expensive because of limited data resources. In such cases, Neural Network
Language Models (NNLMs), generally outperform the traditional non-parametric
N-gram models. Here we investigate the performance of a feed-forward NNLM on an
authorship attribution problem, with moderate author set size and relatively
limited data. We also consider how the text topics impact performance. Compared
with a well-constructed N-gram baseline method with Kneser-Ney smoothing, the
proposed method achieves nearly 2:5% reduction in perplexity and increases
author classification accuracy by 3:43% on average, given as few as 5 test
sentences. The performance is very competitive with the state of the art in
terms of accuracy and demand on test data. The source code, preprocessed
datasets, a detailed description of the methodology and results are available
at https://github.com/zge/authorship-attribution.Comment: Proceedings of the 30th AAAI Conference on Artificial Intelligence
(AAAI'16
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