Incremental Natural Language Processing: Challenges, Strategies, and Evaluation

05/31/2018
by   Arne Köhn, et al.
0

Incrementality is ubiquitous in human-human interaction and beneficial for human-computer interaction. It has been a topic of research in different parts of the NLP community, mostly with focus on the specific topic at hand even though incremental systems have to deal with similar challenges regardless of domain. In this survey, I consolidate and categorize the approaches, identifying similarities and differences in the computation and data, and show trade-offs that have to be considered. A focus lies on evaluating incremental systems because the standard metrics often fail to capture the incremental properties of a system and coming up with a suitable evaluation scheme is non-trivial.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/06/2021

Putting Humans in the Natural Language Processing Loop: A Survey

How can we design Natural Language Processing (NLP) systems that learn f...
research
05/18/2023

TAPIR: Learning Adaptive Revision for Incremental Natural Language Understanding with a Two-Pass Model

Language is by its very nature incremental in how it is produced and pro...
research
06/28/2023

Generative User-Experience Research for Developing Domain-specific Natural Language Processing Applications

User experience (UX) is a part of human-computer interaction (HCI) resea...
research
01/17/2023

The Recent Advances in Automatic Term Extraction: A survey

Automatic term extraction (ATE) is a Natural Language Processing (NLP) t...
research
10/06/2020

A Survey on Recognizing Textual Entailment as an NLP Evaluation

Recognizing Textual Entailment (RTE) was proposed as a unified evaluatio...
research
08/21/2022

A Survey on Transactional Stream Processing

Transactional stream processing (TSP) has been increasingly gaining trac...

Please sign up or login with your details

Forgot password? Click here to reset