Natural Language Processing, Translation, and Iraqi Poetry: Toward a Culturally Aware Digital Framework
TL;DR
The paper argues that NLP should operate as an interpretive assistant rather than an autonomous literary translator in translating Iraqi poetry into English, and proposes a culturally aware, human-in-the-loop framework for supporting literary translation.
Abstract
ABSTRACT Objectives: This article critically examines the role of natural language processing (NLP) in translating Iraqi poetry into English, focusing on its usefulness and its risks. It addresses three linked issues: computational readings of poetic imagery, translation of Iraqi dialectal and cultural markers, and ethical questions raised when automated systems interpret poems shaped by historical memory, place, rhythm, and affect. Methods: The study proposes a culturally aware, human-in-the-loop framework for supporting literary translation. The framework combines corpus preparation, dialect-sensitive annotation, metaphor tracking, named-entity recognition, sentiment and stance analysis, and translator-centered evaluation. The method is illustrated through constructed and paraphrased examples, allowing the framework to be tested later on a verified corpus of Iraqi poems. Results: The analysis indicates that NLP can assist translators by identifying recurrent images, place names, semantic fields, repetition patterns, lexical density, and dialectal signals. These outputs can provide useful linguistic evidence for interpretation. However, standard NLP and machine translation systems often flatten metaphor, misread local references, normalize dialect, ignore prosody, and produce fluent English renderings that reduce cultural resonance. Such distortions are especially serious when Iraqi poetry carries memory of war, displacement, loss, and belonging. Conclusions: The paper argues that NLP should operate as an interpretive assistant rather than an autonomous literary translator. A responsible approach to Iraqi poetry requires a hybrid workflow in which computational tools generate evidence, human translators interpret it with cultural sensitivity, and culturally informed evaluation protects ambiguity, voice, historical specificity, and poetic meaning without replacing the translator's critical and responsible ethical judgement.