INLP Workshop

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.

Particular topics of interest include, but are not limited to:

Interpretability in dialogue systems
Intellectual probing methods for NLP
Interpretability in representation of language models
New metrics and Evaluation in NLP
New tools for interpretable benchmarking

Schedule

The workshop will be structured as a hybrid event – both virtually and physically with in-person attendance at the Stockholm, Sweden conference venue, on June 19, Monday, afternoon Stockholm time. Each presentation is expected to be structured as a talk 45 minutes in duration, including Q&A time at the end. The overall schedule will be aligned to that of the AGI-23 conference.

The following presentations are accepted:

13:25-14:10 - Patrick Hammer and Peter Isaev: "NARS-GPT (with demo)"
14:10-14:55 - Anton Kolonin: "Interpretable Natural Language Processing: Self-tuning hyper-parameters for unsupervised cross-lingual tokenization and morpho-parsing"
14:55-15:10 - BREAK
15:10-15:40 - Zachar Ponimash and Viktor Nosko: "ExplainitAll library: Explainable and interpretable AI for generative transformer neural networks"
15:40-16:10 - Ben Goertzel, Andres Suarez-Madrigal, Man Hin Leung, Amen Belayneh: "Interfacing Linguistics and Logic: Unleashing the Potential of Large Language Models through Augmented AMR and Higher Order Logic"
16:10-16:40 - Nick Mikhailovsky: "Multiscale phenomena in languages and language models"

The workshop has ended. Watch the recording below:

Watch Workshop Recording

Committee

Organizers

Anton Kolonin, Ph.D., AI and Blockchain Architect @ SingularityNET Foundation, Founder @ Aigents, Senior Lecturer and Lead Specialist @ Novosibirsk State University
Linas Vepstas, Ph.D., Research Scientist @ OpenCog Foundation
Tatiana Shavrina, Ph.D.Director of Key Research Projects, NLP @ AIRI
Nick Mikhailovsky, CEO @ NTR Labs
Vignav Ramesh, NLP Research & Development Intern @ SingularityNET Foundation, Contributor @ Aigents

Call for Papers

The paper submission period is now over.

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).