12 research outputs found
Sequence to Sequence Mixture Model for Diverse Machine Translation
Sequence to sequence (SEQ2SEQ) models often lack diversity in their generated
translations. This can be attributed to the limitation of SEQ2SEQ models in
capturing lexical and syntactic variations in a parallel corpus resulting from
different styles, genres, topics, or ambiguity of the translation process. In
this paper, we develop a novel sequence to sequence mixture (S2SMIX) model that
improves both translation diversity and quality by adopting a committee of
specialized translation models rather than a single translation model. Each
mixture component selects its own training dataset via optimization of the
marginal loglikelihood, which leads to a soft clustering of the parallel
corpus. Experiments on four language pairs demonstrate the superiority of our
mixture model compared to a SEQ2SEQ baseline with standard or diversity-boosted
beam search. Our mixture model uses negligible additional parameters and incurs
no extra computation cost during decoding.Comment: 11 pages, 5 figures, accepted to CoNLL201
Oversampling Log Messages Using a Sequence Generative Adversarial Network for Anomaly Detection and Classification
Dealing with imbalanced data is one of the main challenges in machine/deep
learning algorithms for classification. This issue is more important with log
message data as it is typically very imbalanced and negative logs are rare. In
this paper, a model is proposed to generate text log messages using a SeqGAN
network. Then features are extracted using an Autoencoder and anomaly detection
is done using a GRU network. The proposed model is evaluated with two
imbalanced log data sets, namely BGL and Openstack. Results are presented which
show that oversampling and balancing data increases the accuracy of anomaly
detection and classification.Comment: 14 pages, 4 figures, 2 table