Lyan Verwimp


2023

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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.

2018

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State Gradients for RNN Memory Analysis
Lyan Verwimp | Hugo Van hamme | Vincent Renkens | Patrick Wambacq
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

We present a framework for analyzing what the state in RNNs remembers from its input embeddings. We compute the gradients of the states with respect to the input embeddings and decompose the gradient matrix with Singular Value Decomposition to analyze which directions in the embedding space are best transferred to the hidden state space, characterized by the largest singular values. We apply our approach to LSTM language models and investigate to what extent and for how long certain classes of words are remembered on average for a certain corpus. Additionally, the extent to which a specific property or relationship is remembered by the RNN can be tracked by comparing a vector characterizing that property with the direction(s) in embedding space that are best preserved in hidden state space.

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A Comparison of Different Punctuation Prediction Approaches in a Translation Context
Vincent Vandeghinste | Lyan Verwimp | Joris Pelemans | Patrick Wambacq
Proceedings of the 21st Annual Conference of the European Association for Machine Translation

We test a series of techniques to predict punctuation and its effect on machine translation (MT) quality. Several techniques for punctuation prediction are compared: language modeling techniques, such as n-grams and long shortterm memories (LSTM), sequence labeling LSTMs (unidirectional and bidirectional), and monolingual phrase-based, hierarchical and neural MT. For actual translation, phrase-based, hierarchical and neural MT are investigated. We observe that for punctuation prediction, phrase-based statistical MT and neural MT reach similar results, and are best used as a preprocessing step which is followed by neural MT to perform the actual translation. Implicit punctuation insertion by a dedicated neural MT system, trained on unpunctuated source and punctuated target, yields similar results.

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Smart Computer-Aided Translation Environment (SCATE): Highlights
Vincent Vandeghinste | Tom Vanallemeersch | Bram Bulté | Liesbeth Augustinus | Frank Van Eynde | Joris Pelemans | Lyan Verwimp | Patrick Wambacq | Geert Heyman | Marie-Francine Moens | Iulianna van der Lek-Ciudin | Frieda Steurs | Ayla Rigouts Terryn | Els Lefever | Arda Tezcan | Lieve Macken | Sven Coppers | Jens Brulmans | Jan Van Den Bergh | Kris Luyten | Karin Coninx
Proceedings of the 21st Annual Conference of the European Association for Machine Translation

We present the highlights of the now finished 4-year SCATE project. It was completed in February 2018 and funded by the Flemish Government IWT-SBO, project No. 130041.1

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TF-LM: TensorFlow-based Language Modeling Toolkit
Lyan Verwimp | Hugo Van hamme | Patrick Wambacq
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Character-Word LSTM Language Models
Lyan Verwimp | Joris Pelemans | Hugo Van hamme | Patrick Wambacq
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We present a Character-Word Long Short-Term Memory Language Model which both reduces the perplexity with respect to a baseline word-level language model and reduces the number of parameters of the model. Character information can reveal structural (dis)similarities between words and can even be used when a word is out-of-vocabulary, thus improving the modeling of infrequent and unknown words. By concatenating word and character embeddings, we achieve up to 2.77% relative improvement on English compared to a baseline model with a similar amount of parameters and 4.57% on Dutch. Moreover, we also outperform baseline word-level models with a larger number of parameters.

2016

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SCALE: A Scalable Language Engineering Toolkit
Joris Pelemans | Lyan Verwimp | Kris Demuynck | Hugo Van hamme | Patrick Wambacq
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper we present SCALE, a new Python toolkit that contains two extensions to n-gram language models. The first extension is a novel technique to model compound words called Semantic Head Mapping (SHM). The second extension, Bag-of-Words Language Modeling (BagLM), bundles popular models such as Latent Semantic Analysis and Continuous Skip-grams. Both extensions scale to large data and allow the integration into first-pass ASR decoding. The toolkit is open source, includes working examples and can be found on http://github.com/jorispelemans/scale.