8,744 research outputs found
Multitask Learning for Fine-Grained Twitter Sentiment Analysis
Traditional sentiment analysis approaches tackle problems like ternary
(3-category) and fine-grained (5-category) classification by learning the tasks
separately. We argue that such classification tasks are correlated and we
propose a multitask approach based on a recurrent neural network that benefits
by jointly learning them. Our study demonstrates the potential of multitask
models on this type of problems and improves the state-of-the-art results in
the fine-grained sentiment classification problem.Comment: International ACM SIGIR Conference on Research and Development in
Information Retrieval 201
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
Multimodal news article analysis
The intersection of Computer Vision and Natural Language Processing has been a hot topic of research in recent years, with results that were unthinkable only a few years ago. In view of this progress, we want to highlight online news articles as a potential next step for this area of research. The rich interrelations of text, tags, images or videos, as well as a vast corpus of general knowledge are an exciting benchmark for high-capacity models such as the deep neural networks. In this paper we present a series of tasks and baseline approaches to leverage corpus such as the BreakingNews dataset.Peer ReviewedPostprint (author's final draft
Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition
End-to-end training of deep learning-based models allows for implicit
learning of intermediate representations based on the final task loss. However,
the end-to-end approach ignores the useful domain knowledge encoded in explicit
intermediate-level supervision. We hypothesize that using intermediate
representations as auxiliary supervision at lower levels of deep networks may
be a good way of combining the advantages of end-to-end training and more
traditional pipeline approaches. We present experiments on conversational
speech recognition where we use lower-level tasks, such as phoneme recognition,
in a multitask training approach with an encoder-decoder model for direct
character transcription. We compare multiple types of lower-level tasks and
analyze the effects of the auxiliary tasks. Our results on the Switchboard
corpus show that this approach improves recognition accuracy over a standard
encoder-decoder model on the Eval2000 test set
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