Beomseok Lee


2022

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Language Model Augmented Monotonic Attention for Simultaneous Translation
Sathish Reddy Indurthi | Mohd Abbas Zaidi | Beomseok Lee | Nikhil Kumar Lakumarapu | Sangha Kim
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The state-of-the-art adaptive policies for Simultaneous Neural Machine Translation (SNMT) use monotonic attention to perform read/write decisions based on the partial source and target sequences. The lack of sufficient information might cause the monotonic attention to take poor read/write decisions, which in turn negatively affects the performance of the SNMT model. On the other hand, human translators make better read/write decisions since they can anticipate the immediate future words using linguistic information and domain knowledge. In this work, we propose a framework to aid monotonic attention with an external language model to improve its decisions. Experiments on MuST-C English-German and English-French speech-to-text translation tasks show the future information from the language model improves the state-of-the-art monotonic multi-head attention model further.

2020

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End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning
Hou Jeung Han | Mohd Abbas Zaidi | Sathish Reddy Indurthi | Nikhil Kumar Lakumarapu | Beomseok Lee | Sangha Kim
Proceedings of the 17th International Conference on Spoken Language Translation

In this paper, we describe end-to-end simultaneous speech-to-text and text-to-text translation systems submitted to IWSLT2020 online translation challenge. The systems are built by adding wait-k and meta-learning approaches to the Transformer architecture. The systems are evaluated on different latency regimes. The simultaneous text-to-text translation achieved a BLEU score of 26.38 compared to the competition baseline score of 14.17 on the low latency regime (Average latency ≤ 3). The simultaneous speech-to-text system improves the BLEU score by 7.7 points over the competition baseline for the low latency regime (Average Latency ≤ 1000).

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End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning
Nikhil Kumar Lakumarapu | Beomseok Lee | Sathish Reddy Indurthi | Hou Jeung Han | Mohd Abbas Zaidi | Sangha Kim
Proceedings of the 17th International Conference on Spoken Language Translation

In this paper, we describe the system submitted to the IWSLT 2020 Offline Speech Translation Task. We adopt the Transformer architecture coupled with the meta-learning approach to build our end-to-end Speech-to-Text Translation (ST) system. Our meta-learning approach tackles the data scarcity of the ST task by leveraging the data available from Automatic Speech Recognition (ASR) and Machine Translation (MT) tasks. The meta-learning approach combined with synthetic data augmentation techniques improves the model performance significantly and achieves BLEU scores of 24.58, 27.51, and 27.61 on IWSLT test 2015, MuST-C test, and Europarl-ST test sets respectively.