Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916229204148224 |
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| author | Kartik, Kartik Soni, Sanjana Kunchukuttan, Anoop Chakraborty, Tanmoy Akhtar, Md Shad |
| author_facet | Kartik, Kartik Soni, Sanjana Kunchukuttan, Anoop Chakraborty, Tanmoy Akhtar, Md Shad |
| contents | The widespread online communication in a modern multilingual world has provided opportunities to blend more than one language (aka code-mixed language) in a single utterance. This has resulted a formidable challenge for the computational models due to the scarcity of annotated data and presence of noise. A potential solution to mitigate the data scarcity problem in low-resource setup is to leverage existing data in resource-rich language through translation. In this paper, we tackle the problem of code-mixed (Hinglish and Bengalish) to English machine translation. First, we synthetically develop HINMIX, a parallel corpus of Hinglish to English, with ~4.2M sentence pairs. Subsequently, we propose RCMT, a robust perturbation based joint-training model that learns to handle noise in the real-world code-mixed text by parameter sharing across clean and noisy words. Further, we show the adaptability of RCMT in a zero-shot setup for Bengalish to English translation. Our evaluation and comprehensive analyses qualitatively and quantitatively demonstrate the superiority of RCMT over state-of-the-art code-mixed and robust translation methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_16771 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation Kartik, Kartik Soni, Sanjana Kunchukuttan, Anoop Chakraborty, Tanmoy Akhtar, Md Shad Computation and Language Machine Learning The widespread online communication in a modern multilingual world has provided opportunities to blend more than one language (aka code-mixed language) in a single utterance. This has resulted a formidable challenge for the computational models due to the scarcity of annotated data and presence of noise. A potential solution to mitigate the data scarcity problem in low-resource setup is to leverage existing data in resource-rich language through translation. In this paper, we tackle the problem of code-mixed (Hinglish and Bengalish) to English machine translation. First, we synthetically develop HINMIX, a parallel corpus of Hinglish to English, with ~4.2M sentence pairs. Subsequently, we propose RCMT, a robust perturbation based joint-training model that learns to handle noise in the real-world code-mixed text by parameter sharing across clean and noisy words. Further, we show the adaptability of RCMT in a zero-shot setup for Bengalish to English translation. Our evaluation and comprehensive analyses qualitatively and quantitatively demonstrate the superiority of RCMT over state-of-the-art code-mixed and robust translation methods. |
| title | Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2403.16771 |