Sarah Campbell


2022

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Amazon Alexa AI’s System for IWSLT 2022 Offline Speech Translation Shared Task
Akshaya Shanbhogue | Ran Xue | Ching-Yun Chang | Sarah Campbell
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

This paper describes Amazon Alexa AI’s submission to the IWSLT 2022 Offline Speech Translation Task. Our system is an end-to-end speech translation model that leverages pretrained models and cross modality transfer learning. We detail two improvements to the knowledge transfer schema. First, we implemented a new loss function that reduces knowledge gap between audio and text modalities in translation task effectively. Second, we investigate multiple finetuning strategies including sampling loss, language grouping and domain adaption. These strategies aims to bridge the gaps between speech and text translation tasks. We also implement a multi-stage segmentation and merging strategy that yields improvements on the unsegmented development datasets. Results show that the proposed loss function consistently improves BLEU scores on the development datasets for both English-German and multilingual models. Additionally, certain language pairs see BLEU score improvements with specific finetuning strategies.

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Improving Machine Translation Formality Control with Weakly-Labelled Data Augmentation and Post Editing Strategies
Daniel Zhang | Jiang Yu | Pragati Verma | Ashwinkumar Ganesan | Sarah Campbell
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

This paper describes Amazon Alexa AI’s implementation for the IWSLT 2022 shared task on formality control. We focus on the unconstrained and supervised task for en→hi (Hindi) and en→ja (Japanese) pairs where very limited formality annotated data is available. We propose three simple yet effective post editing strategies namely, T-V conversion, utilizing a verb conjugator and seq2seq models in order to rewrite the translated phrases into formal or informal language. Considering nuances for formality and informality in different languages, our analysis shows that a language-specific post editing strategy achieves the best performance. To address the unique challenge of limited formality annotations, we further develop a formality classifier to perform weakly labelled data augmentation which automatically generates synthetic formality labels from large parallel corpus. Empirical results on the IWSLT formality testset have shown that proposed system achieved significant improvements in terms of formality accuracy while retaining BLEU score on-par with baseline.