TY - GEN

T1 - Unsupervised Context-Driven Question Answering Based on Link Grammar

AU - Ramesh, Vignav

AU - Kolonin, Anton

N1 - Publisher Copyright: © 2022, Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - While general conversational intelligence (GCI) can be considered one of the core aspects of artificial general intelligence (AGI), there currently exists minimal overlap between the disciplines of AGI and natural language processing (NLP). Only a few AGI architectures can comprehend and generate natural language, and most NLP systems rely either on hardcoded, specialized rules and frameworks that cannot generalize to the various complex domains of human language or on heavily trained deep neural network models that cannot be interpreted, controlled, or made sense of. In this paper, we propose an interpretable “Contextual Generator” architecture for question answering (QA), built as an extension of the recently published “Generator” algorithm for sentence generation, that produces grammatically valid answers to queries structured as lists of seed words. We demonstrate the potential for this architecture to perform automated, closed-domain QA by detailing results on queries from SingularityNET’s “small world” POC-English corpus and from the Stanford Question Answering Dataset. Overall, our work may bring a greater degree of GCI to proto-AGI NLP pipelines. The proposed QA architecture is open-source and can be found on GitHub under the MIT License at https://github.com/aigents/aigents-java-nlp.

AB - While general conversational intelligence (GCI) can be considered one of the core aspects of artificial general intelligence (AGI), there currently exists minimal overlap between the disciplines of AGI and natural language processing (NLP). Only a few AGI architectures can comprehend and generate natural language, and most NLP systems rely either on hardcoded, specialized rules and frameworks that cannot generalize to the various complex domains of human language or on heavily trained deep neural network models that cannot be interpreted, controlled, or made sense of. In this paper, we propose an interpretable “Contextual Generator” architecture for question answering (QA), built as an extension of the recently published “Generator” algorithm for sentence generation, that produces grammatically valid answers to queries structured as lists of seed words. We demonstrate the potential for this architecture to perform automated, closed-domain QA by detailing results on queries from SingularityNET’s “small world” POC-English corpus and from the Stanford Question Answering Dataset. Overall, our work may bring a greater degree of GCI to proto-AGI NLP pipelines. The proposed QA architecture is open-source and can be found on GitHub under the MIT License at https://github.com/aigents/aigents-java-nlp.

KW - General conversational intelligence

KW - Interpretable natural language processing

KW - Link grammar

KW - Natural language generation

KW - Question answering

UR - http://www.scopus.com/inward/record.url?scp=85123306861&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/30e0abae-7420-3623-89a2-bd6f07ec184c/

U2 - 10.1007/978-3-030-93758-4_22

DO - 10.1007/978-3-030-93758-4_22

M3 - Conference contribution

AN - SCOPUS:85123306861

SN - 978-3-030-93757-7

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 210

EP - 220

BT - Artificial General Intelligence - 14th International Conference, AGI 2021, Proceedings

A2 - Goertzel, Ben

A2 - Iklé, Matthew

A2 - Potapov, Alexey

PB - Springer Science and Business Media Deutschland GmbH

T2 - 14th International Conference on Artificial General Intelligence, AGI 2021

Y2 - 15 October 2021 through 18 October 2021

ER -