Digital Humanities

Be ready to discuss the following questions:

1) How did the Georgetown-IBM Experiment influence the development of Machine Translation? What were its limitations and achievements?

2) Discuss the transition from Rule-Based Machine Translation (RBMT) to Statistical Machine Translation (SMT). What were the key drivers behind this shift?

3) What are the advantages and disadvantages of Neural Machine Translation (NMT) compared to earlier approaches like RBMT and SMT?

4) How has the Cold War and global political context influenced the early development of Machine Translation?

5) What role did Warren Weaver’s 1949 memorandum play in the conceptualization and advancement of Machine Translation?

6) How do hybrid approaches in Machine Translation combine the strengths of RBMT and SMT? What challenges do they face?

7) In what ways have advancements in computational technology shaped the progress of Machine Translation from the 1950s to the present?

8) Discuss the importance of bilingual text corpora in the development of Statistical Machine Translation. How does the quality of these corpora affect translation outcomes?

9) How does Example-Based Machine Translation (EBMT) differ from other approaches like RBMT and SMT? In what scenarios is EBMT particularly useful?

10) What ethical considerations arise from the increasing reliance on Machine Translation, particularly in sensitive or nuanced communication?

11) How does Machine Translation handle idiomatic expressions, and why is this a significant challenge across different MT approaches?

12) What is the impact of low-resource languages on the effectiveness of current Machine Translation systems? How can these challenges be addressed?

13) Discuss the significance of the "black box" nature of Neural Machine Translation. How does this affect transparency and trust in translation results?

14) How might future advancements in Machine Translation technology affect human translators and the translation industry as a whole?

15) What are the potential benefits and risks of using Machine Translation in global diplomacy and international relations?

Recommended reading:

• Garvin, P., & Austin, W. (1967). The Georgetown-IBM Experiment of 1954: An Evaluation in Retrospect.

  • Description: This paper provides a detailed retrospective evaluation of the Georgetown-IBM experiment, discussing its achievements in demonstrating the feasibility of machine translation, as well as its limitations in terms of the simplicity of the translation algorithm.

• Hutchins, W. J. (2004). The Georgetown-IBM experiment demonstrated in January 1954.

  • Description: This paper describes the technical aspects and historical significance of the Georgetown-IBM experiment, highlighting its role in generating public interest and setting expectations for the future of machine translation.

• Gordin, M. (2016). The Dostoevsky Machine in Georgetown: scientific translation in the Cold War.
  Annals of Science, 73, 208-223.

  • Description: This paper examines the Cold War context of the Georgetown-IBM experiment and its implications for the development of machine translation, particularly in relation to the translation of scientific texts.

• Brandwood, L. (1956). Previous Experiments in Mechanical Translation.
  Babel, 2, 125-127.

  • Description: This article reviews the early attempts at machine translation, including the Georgetown-IBM experiment, and discusses the technical challenges and limitations faced by these pioneering efforts.

• Zarechnak, M. (1959). Three Levels of Linguistic Analysis in Machine Translation.
  J. ACM, 6, 24-32.

  • Description: The paper discusses a general analysis technique developed at Georgetown University for machine translation, focusing on structural transfer from the source language to the target language.

• Tucker, A. (1984). A perspective on machine translation: theory and practice.
  Commun. ACM, 27, 322-329.

  • Description: This article provides an overview of the progress in machine translation from its early days, including a discussion of the Georgetown system and its derivatives like SYSTRAN.

• Bennett, W. S. (1995). Machine Translation in North America.

  • Description: This chapter discusses the historical development of machine translation in North America, highlighting key projects and the influence of Warren Weaver's 1949 memorandum on the field.

• Knight, K., & Koehn, P. (2003). What’s New in Statistical Machine Translation.

  • Description: This paper offers a technical overview of the advancements in statistical machine translation (SMT), including the role of bilingual text corpora in developing these systems.

• Chew, P. A. (2020). Unsupervised-learning financial reconciliation: a robust, accurate approach inspired by machine translation.
  Proceedings of the First ACM International Conference on AI in Finance.

  • Description: This paper discusses the parallels between machine translation and financial reconciliation, using unsupervised learning techniques inspired by developments in MT.

• Ornstein, J. (1955). Mechanical Translation: New Challenge to Communication.
  Science, 122 3173, 745-748.

  • Description: The paper highlights the early optimism and challenges in machine translation following the Georgetown-IBM experiment, and the influence of Warren Weaver's ideas on the field.