212 research outputs found
Transfer learning of language-independent end-to-end ASR with language model fusion
This work explores better adaptation methods to low-resource languages using
an external language model (LM) under the framework of transfer learning. We
first build a language-independent ASR system in a unified sequence-to-sequence
(S2S) architecture with a shared vocabulary among all languages. During
adaptation, we perform LM fusion transfer, where an external LM is integrated
into the decoder network of the attention-based S2S model in the whole
adaptation stage, to effectively incorporate linguistic context of the target
language. We also investigate various seed models for transfer learning.
Experimental evaluations using the IARPA BABEL data set show that LM fusion
transfer improves performances on all target five languages compared with
simple transfer learning when the external text data is available. Our final
system drastically reduces the performance gap from the hybrid systems.Comment: Accepted at ICASSP201
Large Margin Neural Language Model
We propose a large margin criterion for training neural language models.
Conventionally, neural language models are trained by minimizing perplexity
(PPL) on grammatical sentences. However, we demonstrate that PPL may not be the
best metric to optimize in some tasks, and further propose a large margin
formulation. The proposed method aims to enlarge the margin between the "good"
and "bad" sentences in a task-specific sense. It is trained end-to-end and can
be widely applied to tasks that involve re-scoring of generated text. Compared
with minimum-PPL training, our method gains up to 1.1 WER reduction for speech
recognition and 1.0 BLEU increase for machine translation.Comment: 9 pages. Accepted as a long paper in EMNLP201
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