Unsupervised Context-Driven Question Answering Based on Link Grammar

Vignav Ramesh, Anton Kolonin

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование


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.

Язык оригиналаанглийский
Название основной публикацииArtificial General Intelligence - 14th International Conference, AGI 2021, Proceedings
РедакторыBen Goertzel, Matthew Iklé, Alexey Potapov
ИздательSpringer Science and Business Media Deutschland GmbH
Число страниц11
ISBN (электронное издание)978-3-030-93758-4
ISBN (печатное издание)978-3-030-93757-7
СостояниеОпубликовано - 2022
Событие14th International Conference on Artificial General Intelligence, AGI 2021 - San Francisco, Соединенные Штаты Америки
Продолжительность: 15 окт. 202118 окт. 2021

Серия публикаций

НазваниеLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Том13154 LNAI
ISSN (печатное издание)0302-9743
ISSN (электронное издание)1611-3349


Конференция14th International Conference on Artificial General Intelligence, AGI 2021
Страна/TерриторияСоединенные Штаты Америки
ГородSan Francisco

Предметные области OECD FOS+WOS



Подробные сведения о темах исследования «Unsupervised Context-Driven Question Answering Based on Link Grammar». Вместе они формируют уникальный семантический отпечаток (fingerprint).