6,856 research outputs found
Auto-Sizing Neural Networks: With Applications to n-gram Language Models
Neural networks have been shown to improve performance across a range of
natural-language tasks. However, designing and training them can be
complicated. Frequently, researchers resort to repeated experimentation to pick
optimal settings. In this paper, we address the issue of choosing the correct
number of units in hidden layers. We introduce a method for automatically
adjusting network size by pruning out hidden units through
and regularization. We apply this method to language modeling and
demonstrate its ability to correctly choose the number of hidden units while
maintaining perplexity. We also include these models in a machine translation
decoder and show that these smaller neural models maintain the significant
improvements of their unpruned versions.Comment: EMNLP 201
Compact Personalized Models for Neural Machine Translation
We propose and compare methods for gradient-based domain adaptation of
self-attentive neural machine translation models. We demonstrate that a large
proportion of model parameters can be frozen during adaptation with minimal or
no reduction in translation quality by encouraging structured sparsity in the
set of offset tensors during learning via group lasso regularization. We
evaluate this technique for both batch and incremental adaptation across
multiple data sets and language pairs. Our system architecture - combining a
state-of-the-art self-attentive model with compact domain adaptation - provides
high quality personalized machine translation that is both space and time
efficient.Comment: Published at the 2018 Conference on Empirical Methods in Natural
Language Processin
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On stopwords, filtering and data sparsity for sentiment analysis of Twitter
Sentiment classification over Twitter is usually affected by the noisy nature (abbreviations, irregular forms) of tweets data. A popular procedure to reduce the noise of textual data is to remove stopwords by using pre-compiled stopword lists or more sophisticated methods for dynamic stopword identification. However, the effectiveness of removing stopwords in the context of Twitter sentiment classification has been debated in the last few years. In this paper we investigate whether removing stopwords helps or hampers the effectiveness of Twitter sentiment classification methods. To this end, we apply six different stopword identification methods to Twitter data from six different datasets and observe how removing stopwords affects two well-known supervised sentiment classification methods. We assess the impact of removing stopwords by observing fluctuations on the level of data sparsity, the size of the classifier’s feature space and its classification performance. Our results show that using pre-compiled lists of stopwords negatively impacts the performance of Twitter sentiment classification approaches. On the other hand, the dynamic generation of stopword lists, by removing those infrequent terms appearing only once in the corpus, appears to be the optimal method to maintaining a high classification performance while reducing the data sparsity and substantially shrinking the feature space
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