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Machine Learning With Google

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Machine Learning With Google

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A New Update

The new version of the Google Translate app that significantly reduces translation revisions and improves the user experience. The research enabling this is presented in two papers. The first formulates an evaluation framework tailored to live translation and develops methods to reduce instability. The second demonstrates that these methods do very well compared to alternatives, while still retaining the simplicity of the original approach. The resulting model is much more stable and provides a noticeably improved reading experience within Google Translate.

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Zero-Flicker Streaming

The combination of masking and biasing, produces a re-translation system with high quality and low latency, while virtually eliminating erasure. The table below shows how the metrics react to the heuristics we introduced and how they compare to the other systems discussed above. The graph demonst...

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131 reads

Real Time Conversion Of Languages We Don't Understand

The transcription feature in the Google Translate app may be used to create a live, translated transcription for events like meetings and speeches, or for a story at the dinner table. In such settings, it is useful for the translated text to be displayed promptly to help keep the reader engaged.

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268 reads

The Performance Measure of Quality

It is important to recognize the inherent trade-offs between these different aspects of quality. Transcribe enables live-translation by stacking machine translation on top of real-time automatic speech recognition. For each update to the recognized transcript, a fresh translation is gene...

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149 reads

The Bottomline

The solution outlined above returns a decent translation very quickly, while allowing it to be revised as more of the source sentence is spoken. The simple structure of re-translation enables the application of our best speech and translation models with minimal effort. However, reducing erasure ...

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135 reads

Evaluating Live Translation: The Metrics

Erasure: Measures the additional reading burden on the user due to instability. It is the number of words that are erased and replaced for every word in the final translation.

Lag: Measures the average time that has passed between when a user utters a word ...

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165 reads

Real-Time Streaming

In our paper, “Re-translation versus Streaming for Simultaneous Translation”, we show that our original “re-translation” approach to live translation can be fine-tuned to reduce erasure and achieve a more favourable erasure/lag/BLEU trade-off. Withou...

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141 reads

The End Game

The end of an on-going translation tends to flicker because it is more likely to have dependencies on source words that have yet to arrive. We reduce this by truncating some number of words from the translation until the end of the source sentence has been observed. This masking process ...

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139 reads

Stabilizing Re-translation

One straightforward solution to reduce erasure is to decrease the frequency with which translations are updated. Along this line, “streaming translation” models (for example, STACL and MILk) intelligentl...

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151 reads

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jessicadelgado

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