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Found 8 papers, 0 papers with code
Automatic Translation for Multiple NLP tasks: a Multi-task Approach to Machine-oriented NMT Adaptation
We address this problem by proposing a multi-task approach to machine-oriented NMT adaptation, which is capable to serve multiple downstream tasks with a single system.
Machine-oriented NMT Adaptation for Zero-shot NLP tasks: Comparing the Usefulness of Close and Distant Languages
In this work, we tackle the problem in a multilingual setting where a single NMT model translates from multiple languages for downstream automatic processing in the target language.
Data Augmentation for End-to-End Speech Translation: FBK@IWSLT ‘19
On the training side, we focused on data augmentation techniques recently proposed for ST and automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Machine Translation for Machines: the Sentiment Classification Use Case
Towards this objective, we present a reinforcement learning technique based on a new candidate sampling strategy, which exploits the results obtained on the downstream task as weak feedback.
Effort-Aware Neural Automatic Post-Editing
For this purpose, following the common approach in multilingual NMT, we prepend a special token to the beginning of both the source text and the MT output indicating the required amount of post-editing.
Multi-source transformer with combined losses for automatic post editing
In the first subtask, our system improves over the baseline up to -5. 3 TER and +8. 23 BLEU points ranking second out of 11 submitted runs.
Dimension Projection among Languages based on Pseudo-relevant Documents for Query Translation
Using top-ranked documents in response to a query has been shown to be an effective approach to improve the quality of query translation in dictionary-based cross-language information retrieval.
