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# Modelling Cross-Situational Learning on Full Sentences in Few Shots with Simple RNNs

Xavier Hinaut, Subba Reddy Oota, Alexandre Variengien, Frédéric Alexandre

► To cite this version:

Xavier Hinaut, Subba Reddy Oota, Alexandre Variengien, Frédéric Alexandre. Modelling Cross-Situational Learning on Full Sentences in Few Shots with Simple RNNs. CogSci 2024 - 46th Annual Meeting of the Cognitive Science Society, Jul 2024, Rotterdam, Netherlands. hal-04906122

HAL Id: hal-04906122

https://hal.science/hal-04906122v1

Submitted on 22 Jan 2025

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Distributed under a Creative Commons Attribution 4.0 International License
**Title**

Modelling Cross-Situational Learning on Full Sentences in Few Shots with Simple RNNs

**Permalink**

https://escholarship.org/uc/item/0665s4vw

**Journal**

Proceedings of the Annual Meeting of the Cognitive Science Society, 46(0)

**Authors**

Hinaut, Xavier
OOOTA, SUBBA REDDY
Variengien, Alexandre
et al.

**Publication Date**

2024

Peer reviewed
# Modelling Cross-Situational Learning on Full Sentences in Few Shots with Simple RNNs

Xavier Hinaut

Inria, Bordeaux, France

SUBBA REDDY OOTA

Inria, Bordeaux, France

Alexandre Variengien

Inria, Bordeaux, France

Frederic Alexandre

Inria-Research, Bordeaux, France, France

## Abstract

How do children bootstrap language through noisy supervision? Most prior works focused on tracking co-occurrences between individual words and referents. We model cross-situational learning (CSL) at sentence level with few (1000) training examples. We compare reservoir computing (RC) and LSTMs on three datasets including complex robotic commands. For most experiments, reservoirs yield superior performance over LSTMs. Surprisingly, reservoirs demonstrate robust generalization when increasing vocabulary size: the error grows slowly. On the contrary, LSTMs are not robust: the number of hidden units needs to be dramatically increased to follow up vocabulary size increase, which is questionable from a biological or cognitive perspective. This suggests that that random projections used in RC helps to bootstrap generalization quickly. To our knowledge, this is a new result in developmental learning modelling. We analyse the evolution of internal representations during training of both recurrent networks and suggest why reservoir generalization seems more efficient.