9 research outputs found
An Empirical Study of End-to-end Simultaneous Speech Translation Decoding Strategies
This paper proposes a decoding strategy for end-to-end simultaneous speech
translation. We leverage end-to-end models trained in offline mode and conduct
an empirical study for two language pairs (English-to-German and
English-to-Portuguese). We also investigate different output token
granularities including characters and Byte Pair Encoding (BPE) units. The
results show that the proposed decoding approach allows to control BLEU/Average
Lagging trade-off along different latency regimes. Our best decoding settings
achieve comparable results with a strong cascade model evaluated on the
simultaneous translation track of IWSLT 2020 shared task.Comment: This paper has been accepted for presentation at IEEE ICASSP 202
Efficient Wait-k Models for Simultaneous Machine Translation
Simultaneous machine translation consists in starting output generation
before the entire input sequence is available. Wait-k decoders offer a simple
but efficient approach for this problem. They first read k source tokens, after
which they alternate between producing a target token and reading another
source token. We investigate the behavior of wait-k decoding in low resource
settings for spoken corpora using IWSLT datasets. We improve training of these
models using unidirectional encoders, and training across multiple values of k.
Experiments with Transformer and 2D-convolutional architectures show that our
wait-k models generalize well across a wide range of latency levels. We also
show that the 2D-convolution architecture is competitive with Transformers for
simultaneous translation of spoken language.Comment: Accepted at INTERSPEECH 202
Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning
We present a novel approach to efficiently learn a simultaneous translation
model with coupled programmer-interpreter policies. First, wepresent an
algorithmic oracle to produce oracle READ/WRITE actions for training bilingual
sentence-pairs using the notion of word alignments. This oracle actions are
designed to capture enough information from the partial input before writing
the output. Next, we perform a coupled scheduled sampling to effectively
mitigate the exposure bias when learning both policies jointly with imitation
learning. Experiments on six language-pairs show our method outperforms strong
baselines in terms of translation quality while keeping the translation delay
low.Comment: 9 page
Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-English
We conduct in this work an evaluation study comparing offline and online
neural machine translation architectures. Two sequence-to-sequence models:
convolutional Pervasive Attention (Elbayad et al. 2018) and attention-based
Transformer (Vaswani et al. 2017) are considered. We investigate, for both
architectures, the impact of online decoding constraints on the translation
quality through a carefully designed human evaluation on English-German and
German-English language pairs, the latter being particularly sensitive to
latency constraints. The evaluation results allow us to identify the strengths
and shortcomings of each model when we shift to the online setup.Comment: Accepted at COLING 202