1 code implementation • LREC 2016 • Joris Pelemans, Lyan Verwimp, Kris Demuynck, Hugo Van hamme, Patrick Wambacq
In this paper we present SCALE, a new Python toolkit that contains two extensions to n-gram language models.
no code implementations • EACL 2017 • Lyan Verwimp, Joris Pelemans, Hugo Van hamme, Patrick Wambacq
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.
no code implementations • 12 Sep 2017 • Lyan Verwimp, Joris Pelemans, Marieke Lycke, Hugo Van hamme, Patrick Wambacq
One model is trained on all available data (46M word tokens), but we also trained models on a specific type of TV show or domain/topic.
no code implementations • WS 2018 • Lyan Verwimp, Hugo Van hamme, Vincent Renkens, Patrick Wambacq
We present a framework for analyzing what the state in RNNs remembers from its input embeddings.
no code implementations • 24 Sep 2018 • Lyan Verwimp, Joris Pelemans, Hugo Van hamme, Patrick Wambacq
Neural cache language models (LMs) extend the idea of regular cache language models by making the cache probability dependent on the similarity between the current context and the context of the words in the cache.
no code implementations • 9 Sep 2019 • Lyan Verwimp, Jerome R. Bellegarda
Natural language processing (NLP) tasks tend to suffer from a paucity of suitably annotated training data, hence the recent success of transfer learning across a wide variety of them.
no code implementations • 14 Feb 2021 • Sashank Gondala, Lyan Verwimp, Ernest Pusateri, Manos Tsagkias, Christophe Van Gysel
We customize entropy pruning by allowing for a keep list of infrequent n-grams that require a more relaxed pruning threshold, and propose three methods to construct the keep list.
no code implementations • 16 Jun 2021 • Wim Boes, Robbe Van Rompaey, Lyan Verwimp, Joris Pelemans, Hugo Van hamme, Patrick Wambacq
We inspect the long-term learning ability of Long Short-Term Memory language models (LSTM LMs) by evaluating a contextual extension based on the Continuous Bag-of-Words (CBOW) model for both sentence- and discourse-level LSTM LMs and by analyzing its performance.
no code implementations • 21 Oct 2022 • Thien Nguyen, Nathalie Tran, Liuhui Deng, Thiago Fraga da Silva, Matthew Radzihovsky, Roger Hsiao, Henry Mason, Stefan Braun, Erik McDermott, Dogan Can, Pawel Swietojanski, Lyan Verwimp, Sibel Oyman, Tresi Arvizo, Honza Silovsky, Arnab Ghoshal, Mathieu Martel, Bharat Ram Ambati, Mohamed Ali
Code-switching describes the practice of using more than one language in the same sentence.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 16 May 2023 • Markus Nußbaum-Thom, Lyan Verwimp, Youssef Oualil
On-device automatic speech recognition systems face several challenges compared to server-based systems.
no code implementations • 27 Mar 2024 • Rricha Jalota, Lyan Verwimp, Markus Nussbaum-Thom, Amr Mousa, Arturo Argueta, Youssef Oualil
Based on this insight and leveraging the design of our production models, we introduce a new architecture for World English NNLM that meets the accuracy, latency, and memory constraints of our single-dialect models.