On the long-term learning ability of LSTM LMs

Wim Boes; Robbe Van Rompaey; Lyan Verwimp; Joris Pelemans; Hugo Van hamme; Patrick Wambacq

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arXiv · 2106.08927

On the long-term learning ability of LSTM LMs

Wim Boes·Robbe Van Rompaey·Lyan Verwimp·Joris Pelemans·Hugo Van hamme·Patrick Wambacq

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Abstract

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. We evaluate on text and speech. Sentence-level models using the long-term contextual module perform comparably to vanilla discourse-level LSTM LMs. On the other hand, the extension does not provide gains for discourse-level models. These findings indicate that discourse-level LSTM LMs already rely on contextual information to perform long-term learning.

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Wim Boes, Robbe Van Rompaey, Lyan Verwimp, Joris Pelemans, Hugo Van hamme, Patrick Wambacq. 2021-06-16. On the long-term learning ability of LSTM LMs. https://arxiv.org/abs/2106.08927

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