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TL;DR

This paper introduces INLP, an intermediate approach to natural language processing that focuses on interactive, goal-oriented communication tailored to users' variable knowledge levels, bridging the gap between human understanding and machine processing.

Contribution

It proposes the concept of INLP as a new paradigm, emphasizing interactive, user-centered NLP that adapts to incomplete knowledge and aims for practical, goal-driven applications.

Findings

INLP is feasible and can be implemented at reasonable cost.

It effectively bridges the gap between human knowledge and machine understanding.

The approach enhances user engagement and goal achievement in NLP tasks.

Abstract

Historically two types of NLP have been investigated: fully automated processing of language by machines (NLP) and autonomous processing of natural language by people, i.e. the human brain (psycholinguistics). We believe that there is room and need for another kind, INLP: interactive natural language processing. This intermediate approach starts from peoples' needs, trying to bridge the gap between their actual knowledge and a given goal. Given the fact that peoples' knowledge is variable and often incomplete, the aim is to build bridges linking a given knowledge state to a given goal. We present some examples, trying to show that this goal is worth pursuing, achievable and at a reasonable cost.