219 research outputs found
A Neural Network Approach for Mixing Language Models
The performance of Neural Network (NN)-based language models is steadily
improving due to the emergence of new architectures, which are able to learn
different natural language characteristics. This paper presents a novel
framework, which shows that a significant improvement can be achieved by
combining different existing heterogeneous models in a single architecture.
This is done through 1) a feature layer, which separately learns different
NN-based models and 2) a mixture layer, which merges the resulting model
features. In doing so, this architecture benefits from the learning
capabilities of each model with no noticeable increase in the number of model
parameters or the training time. Extensive experiments conducted on the Penn
Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a
significant reduction of the perplexity when compared to state-of-the-art
feedforward as well as recurrent neural network architectures.Comment: Published at IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP) 2017. arXiv admin note: text overlap with
arXiv:1703.0806
A Batch Noise Contrastive Estimation Approach for Training Large Vocabulary Language Models
Training large vocabulary Neural Network Language Models (NNLMs) is a
difficult task due to the explicit requirement of the output layer
normalization, which typically involves the evaluation of the full softmax
function over the complete vocabulary. This paper proposes a Batch Noise
Contrastive Estimation (B-NCE) approach to alleviate this problem. This is
achieved by reducing the vocabulary, at each time step, to the target words in
the batch and then replacing the softmax by the noise contrastive estimation
approach, where these words play the role of targets and noise samples at the
same time. In doing so, the proposed approach can be fully formulated and
implemented using optimal dense matrix operations. Applying B-NCE to train
different NNLMs on the Large Text Compression Benchmark (LTCB) and the One
Billion Word Benchmark (OBWB) shows a significant reduction of the training
time with no noticeable degradation of the models performance. This paper also
presents a new baseline comparative study of different standard NNLMs on the
large OBWB on a single Titan-X GPU.Comment: Accepted for publication at INTERSPEECH'1
NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation
Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in
building end-to-end trainable dialogue systems. Though highly efficient in
learning the backbone of human-computer communications, they suffer from the
problem of strongly favoring short generic responses. In this paper, we argue
that a good response should smoothly connect both the preceding dialogue
history and the following conversations. We strengthen this connection through
mutual information maximization. To sidestep the non-differentiability of
discrete natural language tokens, we introduce an auxiliary continuous code
space and map such code space to a learnable prior distribution for generation
purpose. Experiments on two dialogue datasets validate the effectiveness of our
model, where the generated responses are closely related to the dialogue
context and lead to more interactive conversations.Comment: Accepted by EMNLP201
Convolution Kernels for Subjectivity Detection
Proceedings of the 18th Nordic Conference of Computational Linguistics
NODALIDA 2011.
Editors: Bolette Sandford Pedersen, Gunta Nešpore and Inguna Skadiņa.
NEALT Proceedings Series, Vol. 11 (2011), 254-261.
© 2011 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/16955
Sequential Recurrent Neural Networks for Language Modeling
Feedforward Neural Network (FNN)-based language models estimate the
probability of the next word based on the history of the last N words, whereas
Recurrent Neural Networks (RNN) perform the same task based only on the last
word and some context information that cycles in the network. This paper
presents a novel approach, which bridges the gap between these two categories
of networks. In particular, we propose an architecture which takes advantage of
the explicit, sequential enumeration of the word history in FNN structure while
enhancing each word representation at the projection layer through recurrent
context information that evolves in the network. The context integration is
performed using an additional word-dependent weight matrix that is also learned
during the training. Extensive experiments conducted on the Penn Treebank (PTB)
and the Large Text Compression Benchmark (LTCB) corpus showed a significant
reduction of the perplexity when compared to state-of-the-art feedforward as
well as recurrent neural network architectures.Comment: published (INTERSPEECH 2016), 5 pages, 3 figures, 4 table
Predictive Features in Semi-Supervised Learning for Polarity Classification and the Role of Adjectives
Proceedings of the 17th Nordic Conference of Computational Linguistics
NODALIDA 2009.
Editors: Kristiina Jokinen and Eckhard Bick.
NEALT Proceedings Series, Vol. 4 (2009), 198-205.
© 2009 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/9206
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