204,833 research outputs found
Cross Language Text Classification via Subspace Co-Regularized Multi-View Learning
In many multilingual text classification problems, the documents in different
languages often share the same set of categories. To reduce the labeling cost
of training a classification model for each individual language, it is
important to transfer the label knowledge gained from one language to another
language by conducting cross language classification. In this paper we develop
a novel subspace co-regularized multi-view learning method for cross language
text classification. This method is built on parallel corpora produced by
machine translation. It jointly minimizes the training error of each classifier
in each language while penalizing the distance between the subspace
representations of parallel documents. Our empirical study on a large set of
cross language text classification tasks shows the proposed method consistently
outperforms a number of inductive methods, domain adaptation methods, and
multi-view learning methods.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation
In previous works, only parameter weights of ASR models are optimized under
fixed-topology architecture. However, the design of successful model
architecture has always relied on human experience and intuition. Besides, many
hyperparameters related to model architecture need to be manually tuned.
Therefore in this paper, we propose an ASR approach with efficient
gradient-based architecture search, DARTS-ASR. In order to examine the
generalizability of DARTS-ASR, we apply our approach not only on many languages
to perform monolingual ASR, but also on a multilingual ASR setting. Following
previous works, we conducted experiments on a multilingual dataset, IARPA
BABEL. The experiment results show that our approach outperformed the baseline
fixed-topology architecture by 10.2% and 10.0% relative reduction on character
error rates under monolingual and multilingual ASR settings respectively.
Furthermore, we perform some analysis on the searched architectures by
DARTS-ASR.Comment: Accepted at INTERSPEECH 202
Deeper Text Understanding for IR with Contextual Neural Language Modeling
Neural networks provide new possibilities to automatically learn complex
language patterns and query-document relations. Neural IR models have achieved
promising results in learning query-document relevance patterns, but few
explorations have been done on understanding the text content of a query or a
document. This paper studies leveraging a recently-proposed contextual neural
language model, BERT, to provide deeper text understanding for IR. Experimental
results demonstrate that the contextual text representations from BERT are more
effective than traditional word embeddings. Compared to bag-of-words retrieval
models, the contextual language model can better leverage language structures,
bringing large improvements on queries written in natural languages. Combining
the text understanding ability with search knowledge leads to an enhanced
pre-trained BERT model that can benefit related search tasks where training
data are limited.Comment: In proceedings of SIGIR 201
Automated speech and audio analysis for semantic access to multimedia
The deployment and integration of audio processing tools can enhance the semantic annotation of multimedia content, and as a consequence, improve the effectiveness of conceptual access tools. This paper overviews the various ways in which automatic speech and audio analysis can contribute to increased granularity of automatically extracted metadata. A number of techniques will be presented, including the alignment of speech and text resources, large vocabulary speech recognition, key word spotting and speaker classification. The applicability of techniques will be discussed from a media crossing perspective. The added value of the techniques and their potential contribution to the content value chain will be illustrated by the description of two (complementary) demonstrators for browsing broadcast news archives
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