The recent developments in the field of computational linguistics and literary analysis reveal a growing interest in the intersection of language, culture, and technology. Researchers are increasingly leveraging computational methods to explore complex linguistic phenomena, such as intertextuality, rhetorical devices, and the translation of cultural elements. These studies not only advance our understanding of linguistic structures and their preservation across languages but also highlight the limitations of current machine translation technologies in capturing the nuances of cultural expressions. Furthermore, the application of statistical and computational techniques to analyze literary works and oral histories is providing new insights into the mechanisms of narrative cohesion and collective memory, particularly in diasporic communities. This trend underscores the potential of computational tools to enrich our comprehension of language and literature, while also pointing to the need for more culturally aware evaluation metrics in machine translation.

Noteworthy Papers

• Characterizing the Effects of Translation on Intertextuality using Multilingual Embedding Spaces: Introduces a novel metric for analyzing intertextuality in translations, offering both quantitative and qualitative insights into the preservation of rhetorical devices.
• Computational Discovery of Chiasmus in Ancient Religious Text: Presents the first computational method for detecting chiasmus in Biblical texts, achieving high precision and inter-annotator agreement.
• Proverbs Run in Pairs: Evaluating Proverb Translation Capability of Large Language Model: Investigates the translation of proverbs by large language models, revealing their superiority over traditional NMT models and the inadequacy of current evaluation metrics.
• Collective Memory and Narrative Cohesion: A Computational Study of Palestinian Refugee Oral Histories in Lebanon: Uses computational methods to analyze the cohesion of collective memory in Palestinian refugee narratives, highlighting the impact of shared origin and gender.