Robust Neural Machine Translation with Joint Textual and Phonetic Embedding

In this paper, the authors propose a novel approach to neural machine translation that combines textual and phonetic embeddings to improve robustness and accuracy. The proposed method, called joint textual and phonetic embedding (JTPE), leverages the strengths of both modalities to generate high-quality translations in various language pairs.
The authors begin by highlighting the challenges of neural machine translation, particularly in handling out-of-vocabulary words and dealing with diverse linguistic phenomena. They then introduce JTPE as a solution to these problems, which combines textual embeddings (e.g., Word2Vec) with phonetic embeddings (e.g., spectrograms) to generate more robust and accurate translations.
The JTPE model consists of two stages: an attention mechanism that focuses on the most relevant parts of the input sequence, and a neural network that generates the output translation. The attention mechanism is designed to weigh the importance of each word in the input sequence based on its relevance to the current output token, while the neural network uses both textual and phonetic embeddings to generate the final output translation.
The authors evaluate JTPE on several language pairs and show that it outperforms existing state-of-the-art methods in terms of robustness and accuracy. They also perform a series of experiments to analyze the effectiveness of different components of the JTPE model, providing insights into the strengths and limitations of the approach.
Overall, this paper makes an important contribution to the field of neural machine translation by proposing a novel approach that combines textual and phonetic embeddings to improve robustness and accuracy. The proposed method has broad applications in various language translation tasks and has the potential to significantly improve the quality of machine translation systems.