Ashwinkumar Ganesan


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Improving Low Resource Speech Translation with Data Augmentation and Ensemble Strategies
Akshaya Vishnu Kudlu Shanbhogue | Ran Xue | Soumya Saha | Daniel Zhang | Ashwinkumar Ganesan
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

This paper describes the speech translation system submitted as part of the IWSLT 2023 shared task on low resource speech translation. The low resource task aids in building models for language pairs where the training corpus is limited. In this paper, we focus on two language pairs, namely, Tamasheq-French (Tmh→Fra) and Marathi-Hindi (Mr→Hi) and implement a speech translation system that is unconstrained. We evaluate three strategies in our system: (a) Data augmentation where we perform different operations on audio as well as text samples, (b) an ensemble model that integrates a set of models trained using a combination of augmentation strategies, and (c) post-processing techniques where we explore the use of large language models (LLMs) to improve the quality of sentences that are generated. Experiments show how data augmentation can relatively improve the BLEU score by 5.2% over the baseline system for Tmh→Fra while an ensemble model further improves performance by 17% for Tmh→Fra and 23% for Mr→Hi task.


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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.


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Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment
Ashwinkumar Ganesan | Francis Ferraro | Tim Oates
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Locality Preserving Loss: Neighbors that Live together, Align together
Ashwinkumar Ganesan | Francis Ferraro | Tim Oates
Proceedings of the Second Workshop on Domain Adaptation for NLP

We present a locality preserving loss (LPL) that improves the alignment between vector space embeddings while separating uncorrelated representations. Given two pretrained embedding manifolds, LPL optimizes a model to project an embedding and maintain its local neighborhood while aligning one manifold to another. This reduces the overall size of the dataset required to align the two in tasks such as crosslingual word alignment. We show that the LPL-based alignment between input vector spaces acts as a regularizer, leading to better and consistent accuracy than the baseline, especially when the size of the training set is small. We demonstrate the effectiveness of LPL-optimized alignment on semantic text similarity (STS), natural language inference (SNLI), multi-genre language inference (MNLI) and cross-lingual word alignment (CLA) showing consistent improvements, finding up to 16% improvement over our baseline in lower resource settings.