Introduction

While general conversational intelligence (GCI) can be considered one of the core aspects of AGI, the fields of AGI and NLP currently have little overlap, with few existing AGI architectures capable of comprehending natural language and nearly all NLP systems founded upon specialized, hardcoded rules and language-specific frameworks. This workshop is centered around the idea of INLP, an extension of the interpretable AI (IAI) concept to NLP; INLP allows for acquisition of natural language, comprehension of textual communications, and production of textual messages in a reasonable and transparent way. The proposed presentations regarding Link Grammar (LG), unsupervised LG learning, interpretable NLG/NLS, and sentiment mining/topic matching cover various INLP methods that may bring a greater degree of GCI to proto-AGI pipelines.

Committee

Organizers

• Anton Kolonin, Ph.D., AI and Blockchain Architect @ SingularityNET Foundation, Founder @ Aigents, Senior Lecturer and Lead Specialist @ Novosibirsk State University
• Vignav Ramesh, NLP Research & Development Intern @ SingularityNET Foundation, Contributor @ Aigents

Keynote Presenter

• Linas Vepstas, Ph.D., Research Scientist @ OpenCog Foundation

Schedule

Physical attendance (watching the online webinar in a viewing room at the Hilton Garden Inn in Palo Alto) requires registration at http://agi-conf.org/2021/registration/. Virtual/online attendance is completely free of charge and requires registration at https://forms.gle/hVkackmcv6ioBsWw7. The Zoom information for virtual/online attendance will be sent to registrants (those who fill out the INLP registration form) before October 15 via the emails included in registrants’ form responses.

All times below are in Pacific Daylight Time (PDT), on October 15, 2021 - the first day of AGI-21.
Each 1-hour session will include Q&A.

Time	Session Title	Presenter
9:00 AM - 10:00 AM	Explainable Patterns: Unsupervised Learning of Symbolic Representations	Linas Vepstas
10:00 AM - 11:00 AM	Unsupervised Link Grammar Learning	Anton Kolonin
11:00 AM - 12:00 PM	Interpretable Sentiment Mining and Topic Matching in Aigents, Autonio, and SingularityDAO	Anton Kolonin
12:00 PM - 1:00 PM	Lunch
1:00 PM - 2:00 PM	Interpretable Natural Language Generation and Segmentation Using Link Grammar	Vignav Ramesh
2:00 PM - 3:00 PM	Parsing Using a Grammar of Word Association Vectors	Robert Freeman
3:00 PM - 3:30 PM	Wrap Up, Final Q&A	Ramesh, Kolonin, Vepstas, Freeman

Sign Up

The workshop has ended. Watch the recording below:

Watch Workshop Recording

Call for Papers

The paper submission period is now over.

To submit a workshop paper to be included in the AGI-2021 conference proceedings till July 17, follow the AGI-2021 call for paper instructions.

To submit a workshop-only paper after July 17, email inlp21@googlegroups.com the following information:

• Full Name (e.g., John Doe)
• Coauthor Names, comma-separated (e.g., Jacob Smith, Mary Anne)
• Email (e.g., jdoe@google.com)
• Paper Title (e.g., Nerfies: Deformable Neural Radiance Fields)
• Abstract (e.g., We present the first method capable of photorealistically reconstructing a non-rigidly deforming scene using photos/videos captured casually from mobile phones...)
• Full Paper (PDF Format)

Related Work

Publications

• Vepstas, L., Goertzel, B.: Learning Language from a Large (Unannotated) Corpus. arXiv:1401.3372 [cs.LG] (2014).
• Vepstas, L.: Sheaves: A Topological Approach to Big Data. arXiv:1901.01341 [cs.LG] (2019).
• Ramesh, V., Kolonin, A.: Natural Language Generation Using Link Grammar for General Conversational Intelligence. arXiv:2105.00830 [cs.CL] (2021).
• Ramesh, V., Kolonin, A.: Interpretable Natural Language Segmentation Based on Link Grammar. In: 2020 Science and Artificial Intelligence conference (S.A.I.ence), pp. 25–32. IEEE, Novosibirsk (2020).
• Sleator, D., Temperley, D.: Parsing English with a Link Grammar. In: Proceedings of the Third International Workshop on Parsing Technologies, pp. 277–292. Association for Computational Linguistics, Netherlands (1993).
• Lian, R., et al.: Syntax-Semantic Mapping for General Intelligence: Language Comprehension as Hypergraph Homomorphism, Language Generation as Constraint Satisfaction. In: Bach, J., Goertzel, B., Iklé, M. (eds.) ARTIFICIAL GENERAL INTELLIGENCE 2012, LNCS, vol 7716, pp. 158–167. Springer, Heidelberg (2012).
• Glushchenko, A., et al.: Unsupervised Language Learning in OpenCog. In: Iklé M., Franz, A., Rzepka, R., Goertzel, B. (eds.) ARTIFICIAL GENERAL INTELLIGENCE 2018, LNCS, vol 10999, pp. 109–118 Springer, Cham (2018).
• Glushchenko, A., Suarez, A., Kolonin, A., Goertzel, B., Baskov, O.: Programmatic Link Grammar Induction for Unsupervised Language Learning. In: Hammer, P., Agrawal, P., Goertzel, B., Iklé, M. (eds.) ARTIFICIAL GENERAL INTELLIGENCE 2019, LNCS, vol 11654, pp. 111-120. Springer, Cham (2019).
• Vepstas, L.: Sheaves: A Topological Approach to Big Data. arXiv:1901.01341 [cs.LG] (2019).
• Lian, R., Goertzel, B., Vepstas, L., Hanson, D., Zhou, C.: Symbol Grounding via Chaining of Morphisms. arXiv:1703.04368 [cs.AI] (2017).
• Vepstas, L., Goertzel, B.: Learning Language from a Large (Unannotated) Corpus. arXiv:1401.3372 [cs.CL] (2014).
• Goertzel, B., et al.: A General Intelligence Oriented Architecture for Embodied Natural Language Processing. In: Proceedings of the 3rd Conference on Artificial General Intelligence, pp. 68-73. Atlantis Press (2010).
• Goertzel, B., Suarez, A., Yu, G.: Guiding Symbolic Natural Language Grammar Induction via Transformer-Based Sequence Probabilities. arXiv:2005.12533 [cs.CL] (2020).
• Freeman, R.: Parsing using a grammar of word association vectors. arXiv:1403.2152 [cs.CL] (2014).

Presentations

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Contact

To contact the INLP organizing committee, email inlp21@googlegroups.com.