195 research outputs found
The Influences of the Celtic Languages on Present-Day English
This work surveys the state of research on contact influences of Celtic languages on English. Beginning with an overview of the main theories of language contact in general and their influence on the present problem, a historical framework is then laid out. Theories concerning historical language contact scenatios are presented. Possible contact features are discussed, ranging from Syntax and Morphology to Phonology and Loanwords
Adapting End-to-End Speech Recognition for Readable Subtitles
Automatic speech recognition (ASR) systems are primarily evaluated on
transcription accuracy. However, in some use cases such as subtitling, verbatim
transcription would reduce output readability given limited screen size and
reading time. Therefore, this work focuses on ASR with output compression, a
task challenging for supervised approaches due to the scarcity of training
data. We first investigate a cascaded system, where an unsupervised compression
model is used to post-edit the transcribed speech. We then compare several
methods of end-to-end speech recognition under output length constraints. The
experiments show that with limited data far less than needed for training a
model from scratch, we can adapt a Transformer-based ASR model to incorporate
both transcription and compression capabilities. Furthermore, the best
performance in terms of WER and ROUGE scores is achieved by explicitly modeling
the length constraints within the end-to-end ASR system.Comment: IWSLT 202
Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection
Encoder-decoder models provide a generic architecture for
sequence-to-sequence tasks such as speech recognition and translation. While
offline systems are often evaluated on quality metrics like word error rates
(WER) and BLEU, latency is also a crucial factor in many practical use-cases.
We propose three latency reduction techniques for chunk-based incremental
inference and evaluate their efficiency in terms of accuracy-latency trade-off.
On the 300-hour How2 dataset, we reduce latency by 83% to 0.8 second by
sacrificing 1% WER (6% rel.) compared to offline transcription. Although our
experiments use the Transformer, the hypothesis selection strategies are
applicable to other encoder-decoder models. To avoid expensive re-computation,
we use a unidirectionally-attending encoder. After an adaptation procedure to
partial sequences, the unidirectional model performs on-par with the original
model. We further show that our approach is also applicable to low-latency
speech translation. On How2 English-Portuguese speech translation, we reduce
latency to 0.7 second (-84% rel.) while incurring a loss of 2.4 BLEU points (5%
rel.) compared to the offline system
Continuous Learning in Neural Machine Translation using Bilingual Dictionaries
While recent advances in deep learning led to significant improvements in
machine translation, neural machine translation is often still not able to
continuously adapt to the environment. For humans, as well as for machine
translation, bilingual dictionaries are a promising knowledge source to
continuously integrate new knowledge. However, their exploitation poses several
challenges: The system needs to be able to perform one-shot learning as well as
model the morphology of source and target language.
In this work, we proposed an evaluation framework to assess the ability of
neural machine translation to continuously learn new phrases. We integrate
one-shot learning methods for neural machine translation with different word
representations and show that it is important to address both in order to
successfully make use of bilingual dictionaries. By addressing both challenges
we are able to improve the ability to translate new, rare words and phrases
from 30% to up to 70%. The correct lemma is even generated by more than 90%.Comment: 9 pages, EACL 202
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