Computational linguistics connects linguistic theories, like grammar and meaning, with practical tools such as machine translation and speech recognition. These tools are ...
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Computational linguistics connects linguistic theories, like grammar and meaning, with practical tools such as machine translation and speech recognition. These tools are widely used, from translating languages to powering voice assistants. However, putting these theories into practice faces challenges like handling ambiguous language, adapting models for less common languages, and making systems work efficiently in real-time settings (Jurafsky & Martin, 2009; Anastasopoulos et al., 2019).
Major challenges include the need for high-quality data, especially for underrepresented languages, and dealing with the complexity of natural language, such as multiple meanings and context. Implementing these systems also requires addressing issues like scaling them up and ensuring they are fair and ethical, by minimizing bias and protecting user privacy (Bender et al., 2021). Overcoming these obstacles is crucial for creating effective, reliable, and fair language tools that meet the diverse needs of users worldwide.
References:
1. Jurafsky, D., & Martin, J. H. (2009). Speech and Language Processing. Prentice Hall.
2. Anastasopoulos, A., Chiang, D., & Neubig, G. (2019). Tied Multitask Learning for Low-Resource Neural Machine Translation. Association for Computational Linguistics.
3. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency.
