Children’s Sentential Complement Use Leads the Theory of Mind Development Period: Evidence from the CHILDES Corpus

Converging evidence suggests that children’s linguistic and theory of mind (ToM) development are linked. Specifically, learning the sentential complement grammatical structure has been shown to play a causal role in the development of some false belief reasoning skills. Here, we extend this line of work to examine this relationship in the wild by means of a corpus analysis of children’s speech during the typical period of ToM development. We show that children’s use of the sentential complement grammatical structure increases immediately preceding the ToM development period and plateaus shortly thereafter. Furthermore, we find that parents’ child-directed speech follows a similar pattern

Neural Analogical Matching

Analogy is core to human cognition. It allows us to solve problems based on prior experience, it governs the way we conceptualize new information, and it even influences our visual perception. The importance of analogy to humans has made it an active area of research in the broader field of artificial intelligence, resulting in data-efficient models that learn and reason in human-like ways. While cognitive perspectives of analogy and deep learning have generally been studied independently of one another, the integration of the two lines of research is a promising step towards more robust and efficient learning techniques. As part of a growing body of research on such an integration, we introduce the Analogical Matching Network: a neural architecture that learns to produce analogies between structured, symbolic representations that are largely consistent with the principles of Structure-Mapping Theory.Comment: AAAI versio

Applications of Rhetorical Structure Theory in Text Generation

Natural Langauge Generation, also known as text generation, deals with the use of computers to convey information in human language. The problem is a sizable one and touches on many aspects of computer science and linguistics, ranging from Information Retrieval to the rules of grammar. One of the necessary components of the NLG process is a detailed, automated version of the outlining performed by human authors: document planning. To plan a document, a text generation program must have a model of its internal structure. Rhetorical Structure Theory offers such a model, as well as a soft guarantee that the resulting text will be coherent. In this paper I will discuss some of the challenges of applying Rhetorical Structure Theory to Natural Language Generation. Section 2 will contain background on NLG, RST, and the problems and deficiencies that stem from mixing the two. Section 3 will outline the structure of a text generation program, where RST fits into the pipeline, and how other theories can shore up its deficiencies. Section 4 will address the compromises necessary to apply RST to NLG, the problems that remain, and the approaches available to handle them. Finally I will conclude that using RST as the basis of a text generation system leaves much to be desired, and that future efforts are better spent improving existing systems

Using Large Language Models in the Companion Cognitive Architecture: A Case Study and Future Prospects

The goal of the Companion cognitive architecture is to understand how to create human-like software social organisms. Thus natural language capabilities, both for reading and conversation, are essential. Recently we have begun experimenting with large language models as a component in the Companion architecture. This paper summarizes a case study indicating why we are currently using BERT with our symbolic natural language understanding system. It also describes some additional ways we are contemplating using large language models with Companions

Population Bias in Geotagged Tweets

Geotagged tweets are an exciting and increasingly popular data source, but like all social media data, they potentially have biases in who are represented. Motivated by this, we investigate the question, 'are users of geotagged tweets randomly distributed over the US population'? We link approximately 144 million geotagged tweets within the US, representing 2.6m unique users, to high-resolution Census population data and carry out a statistical test by which we answer this question strongly in the negative. We utilize spatial models and integrate further Census data to investigate the factors associated with this nonrandom distribution. We find that, controlling for other factors, population has no effect on the number of geotag users, and instead it is predicted by a number of factors including higher median income, being in an urban area, being further east or on a coast, having more young people, and having high Asian, Black or Hispanic/Latino populations

Corrective Processes in Modeling Reference Resolution

Reference resolution is one of the core components of language understanding. In spite of its centrality, psychological evidence has shown that the reference resolution process is prone to errors and egocentric bias. In this work, we propose an extension to Analogical Reference Resolution, a computational model based on analogical retrieval, which accounts for such errors. We test the extended model on a study by Epley et al. (2004) and replicate human patterns of bias and correction

Rich People Don’t Have More Followers! Overcoming Social Inequality With Social Media

<p>Previous work on personal networks has shown that higher socioeconomic status results in larger and more powerful networks. With the Internet, in particular with social media, it has become easier to establish and maintain relationships, suggesting an equalizing effect. However, people of different socioeconomic status use these new resources in different ways creating a digital divide. In this article we study popularity on Twitter based on estimated socioeconomic status in real life. We collect 1 billion geo-coded Tweets from the United States and connect the geographic position of the sender with socioeconomic data at the level of Census block groups. We show that people tweeting from higher income areas do not have more followers. Rather, there is a small negative correlation between income and number of followers.</p