Publication Details
i-vectors in language modeling: An efficient way of domain adaptation for feed-forward models
language modeling, feed-forward models, subspace multinomial model, domain adaptation
We show an effective way of adding context information to shallow neural language models. We propose to use Subspace Multinomial Model (SMM) for context modeling and we add the extracted i-vectors in a computationally efficient way. By adding this information, we shrink the gap between shallow feed-forward network and an LSTM from 65 to 31 points of perplexity on the Wikitext-2 corpus (in the case of neural 5-gram model). Furthermore, we show that SMM i-vectors are suitable for domain adaptation and a very small amount of adaptation data (e.g. endmost 5% of a Wikipedia article) brings a substantial improvement. Our proposed changes are compatible with most optimization techniques used for shallow feedforward LMs.
@inproceedings{BUT155102,
author="Karel {Beneš} and Santosh {Kesiraju} and Lukáš {Burget}",
title="i-vectors in language modeling: An efficient way of domain adaptation for feed-forward models",
booktitle="Proceedings of Interspeech 2018",
year="2018",
journal="Proceedings of Interspeech",
volume="2018",
number="9",
pages="3383--3387",
publisher="International Speech Communication Association",
address="Hyderabad",
doi="10.21437/Interspeech.2018-1070",
issn="1990-9772",
url="https://www.isca-speech.org/archive/Interspeech_2018/abstracts/1070.html"
}