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Found 9 papers, 3 papers with code
Adding Interpretable Attention to Neural Translation Models Improves Word Alignment
Multi-layer models with multiple attention heads per layer provide superior translation quality compared to simpler and shallower models, but determining what source context is most relevant to each target word is more challenging as a result.
Subword and Crossword Units for CTC Acoustic Models
This paper proposes a novel approach to create an unit set for CTC based speech recognition systems.
Comparison of Decoding Strategies for CTC Acoustic Models
The CTC loss function maps an input sequence of observable feature vectors to an output sequence of symbols.
KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning
{\%} Combining these techniques, we are able to provide an adapted speech translation system for several European languages.
End-to-End Neural Word Alignment Outperforms GIZA++
Although unnecessary for training neural MT models, word alignment still plays an important role in interactive applications of neural machine translation, such as annotation transfer and lexicon injection.
Automatic Bilingual Markup Transfer
We describe the task of bilingual markup transfer, which involves placing markup tags from a source sentence into a fixed target translation.
The 2016 KIT IWSLT Speech-to-Text Systems for English and German
For the English TED task, our best combination system has a WER of 7. 8% on the development set while our other combinations gained 21. 8% and 28. 7% WERs for the English and German MSLT tasks.
The 2017 KIT IWSLT Speech-to-Text Systems for English and German
For the English lecture task, our best combination system has a WER of 8. 3% on the tst2015 development set while our other combinations gained 25. 7% WER for German lecture tasks.
Most discriminative stimuli for functional cell type clustering
Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed.
