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Area of Science:

  • Computational Linguistics
  • Pragmatics
  • Human-Computer Interaction

Background:

  • Investigates artificial intelligence speaker (AIS) verbal communication using real conversation data.
  • Applies Grice's conversational theory to categorize AI errors as maxim violations.
  • Utilizes data from 20 Korean participants interacting with Kakao Mini AIS.

Purpose of the Study:

  • To pragmatically evaluate the verbal communicative performance of an AI speaker (AIS).
  • To categorize AI conversational failures based on Grice's conversational maxims.
  • To quantify user perceptions of AI-generated utterances.

Main Methods:

  • Analyzed 1,026 AI-human dialogues, decomposed into 3,365 adjacency pairs.
  • Classified pairs as conversational success or failure based on AI appropriateness.
  • Conducted an acceptability rating test on 1,024 adjacency pairs for user evaluation.

Main Results:

  • The "maxim of relation" was the most frequently violated conversational maxim by AIS.
  • Violations of the "maxim of relation" were perceived as least natural by language users.
  • AIS conversational failures were primarily linked to irrelevant responses.

Conclusions:

  • AI speakers frequently fail to maintain conversational relevance, impacting naturalness.
  • Developing AI algorithms for contextually relevant utterance generation is crucial for improving communication.
  • The pragmatic evaluation framework is valuable for assessing current and future AI linguistic competence.