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HUMAN-AI COMMUNICATION: SIGN INTERPRETATION
CHALLENGES AND CONTEXTUAL UNDERSTANDING IN DIALOGUE
PhD Candidate Nariste Alieva
Ala-Too International University
+996775585700
Annotation:
This study presents an in-depth analysis of the sign interpretation
mechanisms that are found within human and AI interaction using semiotics, cognitive
and neurolinguistics, as well as pragmatics perspectives. Research conducted brings to
light important critical issues such as the specificity of AI in syntactic and semantic
text analysis, or issues regarding processing polysemous words and cultural codes, or
even avenues through which AI can handle pragmatic intent interpretation. Particular
emphasis is given to analyses of the issues related to cultural noise and intercultural
communication problems in automated dialogues, along with their implications on the
effectiveness of communication.
Keywords:
artificial intelligence; natural language processing; cognitive
linguistics; semiotics; pragmatics; sign interpretation; polysemy; neurolinguistics;
intercultural communication; conversational systems.
Аннотация:
В данном исследовании анализируются механизмы
интерпретации знаков в семантическом поле двусторонней коммуникации между
человеком и искусственным интеллектом с опорой на методы семиотики,
когнитивной лингвистики и нейролингвистики. В частности особое внимание
уделено семантической двусмысленности, многозначности, а также
контекстуальной вариативности значения и специфическим кодам традиционной
и современной культуры, которые влияют на процесс декодирования
коммуникативных аспектов взаимодействия человека и ИИ. Исследование
затрагивает ключевые вопросы, касающиеся роли ИИ в семантическом и
синтаксическом анализе текстов, а также, трудностей, связанных с обработкой
многозначных слов, культурных кодов и способов интерпретации
прагматических аспектов коммуникации. Отдельное внимание уделяется
влиянию культурного шума и особенностей межкультурной коммуникации в
автоматических диалогах и их влияние на эффективность коммуникации.
Ключевые слова:
искусственный интеллект; обработка естественного
языка; когнитивная лингвистика; семиотика; прагматика; интерпретация знаков;
многозначность; нейролингвистика; межкультурная коммуникация; разговорные
системы.
Abstract:
Diese Studie bietet eine eingehende Analyse der Mechanismen der
Zeicheninterpretation in der Mensch-KI-Interaktion unter Einbeziehung semiotischer,
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kognitions- und neurolinguistischer sowie pragmatischer Perspektiven. Besondere
Aufmerksamkeit wird der semantischen Mehrdeutigkeit, der Polysemie, der
kontextuellen Bedeutungsvariabilität sowie den kulturspezifischen Codes gewidmet,
die den Dekodierungsprozess kommunikativer Intentionen beeinflussen. Die Studie
stützt sich auf direkte Mensch-KI-Interaktionen und analysiert reale Fragen und
Antworten, die von KI generiert wurden. Diese empirischen Beispiele bieten eine
Grundlage zur Bewertung, wie KI linguistische Strukturen verarbeitet, Bedeutungen
erkennt und an welchen Stellen Interpretationsfehler auftreten.
Schlüsselwörter:
Künstliche Intelligenz; Verarbeitung natürlicher Sprache;
kognitive Linguistik; Semiotik; Pragmatik; Zeicheninterpretation; Polysemie;
Neurolinguistik; interkulturelle Kommunikation; dialogbasierte Systeme.
Natural Language Processing (NLP) in its increasing rapidity has had quite
positive impacts on human and machine interaction, a domain where artificial
intelligence operates. The most striking advancement in the field is transformer-based
models such as the GPT (Radford et al., 2019; [2:9-10]), which can generate coherent
and contextually relevant text, conduct dialogue, and perform complex cognitive tasks.
However, the progress still leaves open areas of concern regarding the interpretation of
linguistic signs, meanings, and contexts in human-AI communication (Bender &
Koller, 2020; [1:1-9]).
Semiotic analysis of human-AI communication is a major area of research where
mechanisms of sign transmission and interpretation are understood for the digital
environments. Semiotics serves as a valuable tool to investigate AI from a
multidimensional perspective. Through the use of the semiotic analysis, researchers
unravel intricate meanings, biases, as well as cultural implications that underlie modern
AI technologies (Boero, M. and Greco, C., 2023; [4:13]).
One of the major challenges has to do with "cultural noise" in which AI models
fail to give an adequate recognition to the cultural-social codes in the processing of the
linguistic data (Kobiruzzaman, M. M., 2021;[5:7]). While AI will continue to advance,
it is crucially important to keep critical reflection on how signs, symbols are
constructed, coded and decoded between humans and AI (José L. Cendejas Valdez &
Gustavo A. López Saldaña, 2024; [3:493]).
The research was developed through direct human-AI interactions, analyzing
real-world questions and responses generated by AI. Such instances provide a basis for
assessment over how the AI processes linguistic structures, recognizes meaning, and
where breakdowns in the interpretation occur.
As the main argument of the study, human-AI communication contains myriad
competing readings of signs, symbols, and contexts. The study revealed several key
findings: polysemous problems, contextualization troubles, dependence on training
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data, need for improved multilayer interpretation, cultural and social context as a key
variable. Thus the study of human-AI interactivity not only show-cased the strengths
and weaknesses of the current AI technologies with regard to semantics, pragmatics,
and cultural interpretation but also unfolded the future pathways of theorization and
modelling of AI capable of interpreting signs in a better way in multilayered modes of
communication.
Even at the technical level, through to cultural cognition, problems of
interpretation arise from the AI's inability to read the context or the irony or the
metaphor or the cultural codes inherent to particular groups. One particularly important
finding is the need for improvement in the cultural models behind AI systems. There
has certainly been progress, but AI's inability to correctly interpret cultural codes,
memes, humor, and symbols inevitably brings forth misunderstandings and incorrect
judgments, greatly impeding progress. The way forward is through further research and
integration of even richer and more diverse training datasets.
REFERENCES:
1.
Bender, E. M., & Koller, A. (2020). Climbing towards NLU: On meaning,
form, and understanding in the age of data. Proceedings of the 58th Annual Meeting of
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2.
Radford, A., et al. (2019). Language models are unsupervised multitask
learners. OpenAI Blog.
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José L. Cendejas Valdez & Gustavo A. López Saldaña (2024). Semiotics
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Boero, M. and Greco, C. (2023), "Semiotics, Artificial Intelligence,
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Kobiruzzaman, M. M. (2021). Communication Noise- 5 Types of Noises
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