Are You Flirting, Objectifying or What? a Conversation Analysis of “you’re very sexy” Conversational Turn

Kadek Ratih Dwi Oktarini

doi:10.31940/soshum.v10i3.1972

Abstract

Intent identification is one of the most critical components in conversational agent design. Conversational agent “is any dialogue system that not only conducts natural language processing but also responds automatically using human language.” (Conversational Agent, 2019). The crux of designing human-like conversational agent is to mimic how human understands another human and then responds “naturally”. The current study attempts to answer the fundamental question: how to model human processes of understanding another human? In order to answer that question, it starts from exploring some basic concepts relevant to intent identification from Conversation Analysis (CA). CA is a mature field that studies authentic human interaction. The basic concepts from CA are then synthesised into a model that potentially fit to existing framework and paradigm in conversational agent design, i.e. Natural Conversation Framework (NCF) and Intent-Entity-Context-Response (IECR) paradigm. Instead of using a made-up sentence, the model is then tested to an authentic conversational turn seksi sekali dirimu ‘you’re very sexy’. The test shows that the model is able to detect several possible intents contain in this authentic conversational turn. The model is also able to handle Conversational Indonesian and multi-modality. Considering the versatility of Conversation Analysis, in all likelihood the model will be able to handle any language and all kinds of modalities. Future study can be done to analyse more Conversational Indonesian data (to develop library of intent for Conversational Indonesian Language), as well as conversational data from different languages and conversational data containing diverse modalities.

Highlights

With the advancement of Natural Language Processing (NLP) and computational power in general, conversational agent becomes ubiquitous
A considerable number will employ a method of Conversation Analysis (CA)
The current study attends to the question of how to model human processes of understanding another human? In answering this question, the current study has proposed an intent identification model for Conversational Agent, informed by the empirically grounded Conversation Analysis method

Summary

Introduction

With the advancement of Natural Language Processing (NLP) and computational power in general, conversational agent becomes ubiquitous. Conversational agents are doing well in specific tasks. In phone-banking, we give our information to Conversational Agent before talking to human customer service. That person is Conversational Agent of some sort. The downside is that we may still notice that those Conversational Agents are incapable of understanding complex messages. We may spend more time than needed if we talk to a Conversational Agent than speaking to human customer service. One vital issue is to design a Conversational Agent or system that can understand and respond to human the way human do

Methods

Discussion

Conclusion