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Found 15 papers, 4 papers with code
Monolingual Embeddings for Low Resourced Neural Machine Translation
When only little data exist for a language pair, the model cannot produce good representations for words, particularly for rare words.
On Target Segmentation for Direct Speech Translation
Then, subword-level segmentation became the state of the art in neural machine translation as it produces shorter sequences that reduce the training time, while being superior to word-level models.
Contextualized Translation of Automatically Segmented Speech
We show that our context-aware solution is more robust to VAD-segmented input, outperforming a strong base model and the fine-tuning on different VAD segmentations of an English-German test set by up to 4. 25 BLEU points.
Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus
Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines.
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020
The test talks are provided in two versions: one contains the data already segmented with automatic tools and the other is the raw data without any segmentation.
Instance-Based Model Adaptation For Direct Speech Translation
Despite recent technology advancements, the effectiveness of neural approaches to end-to-end speech-to-text translation is still limited by the paucity of publicly available training corpora.
Robust Neural Machine Translation for Clean and Noisy Speech Transcripts
Our experimental results on a public speech translation data set show that adapting a model on a significant amount of parallel data including ASR transcripts is beneficial with test data of the same type, but produces a small degradation when translating clean text.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
One-To-Many Multilingual End-to-end Speech Translation
Multilingual solutions are widely studied in MT and usually rely on ``\textit{target forcing}'', in which multilingual parallel data are combined to train a single model by prepending to the input sequences a language token that specifies the target language.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors
Machine translation systems are conventionally trained on textual resources that do not model phenomena that occur in spoken language.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Effectiveness of Data-Driven Induction of Semantic Spaces and Traditional Classifiers for Sarcasm Detection
Irony and sarcasm are two complex linguistic phenomena that are widely used in everyday language and especially over the social media, but they represent two serious issues for automated text understanding.
Fine-tuning on Clean Data for End-to-End Speech Translation: FBK @ IWSLT 2018
This paper describes FBK's submission to the end-to-end English-German speech translation task at IWSLT 2018.
Deep Neural Machine Translation with Weakly-Recurrent Units
Recurrent neural networks (RNNs) have represented for years the state of the art in neural machine translation.
