Youssef Oualil


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Application-Agnostic Language Modeling for On-Device ASR
Markus Nussbaum-thom | Lyan Verwimp | Youssef Oualil
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)

On-device automatic speech recognition systems face several challenges compared to server-based systems. They have to meet stricter constraints in terms of speed, disk size and memory while maintaining the same accuracy. Often they have to serve several ap- plications with different distributions at once, such as communicating with a virtual assistant and speech-to-text. The simplest solution to serve multiple applications is to build application-specific (language) models, but this leads to an increase in memory. Therefore, we explore different data- and architecture-driven language modeling approaches to build a single application-agnostic model. We propose two novel feed-forward architectures that find an optimal trade off between different on-device constraints. In comparison to the application-specific solution, one of our novel approaches reduces the disk size by half, while maintaining speed and accuracy of the original model.


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The Metalogue Debate Trainee Corpus: Data Collection and Annotations
Volha Petukhova | Andrei Malchanau | Youssef Oualil | Dietrich Klakow | Saturnino Luz | Fasih Haider | Nick Campbell | Dimitris Koryzis | Dimitris Spiliotopoulos | Pierre Albert | Nicklas Linz | Jan Alexandersson
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Long-Short Range Context Neural Networks for Language Modeling
Youssef Oualil | Mittul Singh | Clayton Greenberg | Dietrich Klakow
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Sub-Word Similarity based Search for Embeddings: Inducing Rare-Word Embeddings for Word Similarity Tasks and Language Modelling
Mittul Singh | Clayton Greenberg | Youssef Oualil | Dietrich Klakow
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Training good word embeddings requires large amounts of data. Out-of-vocabulary words will still be encountered at test-time, leaving these words without embeddings. To overcome this lack of embeddings for rare words, existing methods leverage morphological features to generate embeddings. While the existing methods use computationally-intensive rule-based (Soricut and Och, 2015) or tool-based (Botha and Blunsom, 2014) morphological analysis to generate embeddings, our system applies a computationally-simpler sub-word search on words that have existing embeddings. Embeddings of the sub-word search results are then combined using string similarity functions to generate rare word embeddings. We augmented pre-trained word embeddings with these novel embeddings and evaluated on a rare word similarity task, obtaining up to 3 times improvement in correlation over the original set of embeddings. Applying our technique to embeddings trained on larger datasets led to on-par performance with the existing state-of-the-art for this task. Additionally, while analysing augmented embeddings in a log-bilinear language model, we observed up to 50% reduction in rare word perplexity in comparison to other more complex language models.