Abstract. We present a mathematical model of cross-situational learning, in which we quantify the learnability of words and vocabularies. We find that high levels of uncertainty are not an impediment to learning single words or whole vocabulary systems, as long as the level of uncertainty is somewhat lower than the total number of meanings in the system. We further note that even large vocabularies are learnable through cross-situational learning. 1

What are the origins of abstract concepts such as “seven, ” and what role does language play in their development? These experiments probed the natural number words and concepts of 3-year-old children who can recite number words to ten but who can comprehend only one or two. Children correctly judged that a set labeled eight retains this label if it is unchanged, that it is not also four, and that eight is more than two. In contrast, children failed to judge that a set of 8 objects is better labeled by eight than by four, that eight is more than four, that eight continues to apply to a set whose members are rearranged, or that eight ceases to apply if the set is increased by 1, doubled, or halved. The latter errors contrast with children’s correct application of words for the smallest numbers. These findings suggest that children interpret number words by relating them to 2 distinct preverbal systems that capture only limited numerical information. Children construct the system of abstract, natural number concepts from these foundations.

Institute for Research in Cognitive Science (IRCS) at Penn under the direction of Aravind Joshi and Lila Gleitman for providing resources, a constant stream of speakers and visitors, and a vibrant community which fostered my interest in interdisciplinary work. I want to thank my advisors Lila Gleitman, Robin Clark, and Henry Gleitman for their guidance of this dissertation as well as of my graduate career more generally. I thank the members of the Linguistics department and the IRCS community who provided support at all levels. Special thanks go to the members past and present of the Babylab and Cheese groups, and to Sabine

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For over four decades, our research community has taken remarkable strides in advancing human language technologies. This has resulted in the emergence of spoken dialogue interfaces that can communicate with humans on their own terms. For the most part, however, we have assumed that these interfaces are static; it knows what it knows and doesn’t know what it doesn’t. In my opinion, we are not likely to succeed until we can build interfaces that behave more like organisms that can learn, grow, reconfigure, and repair themselves, much like humans. In this paper, I will argue my case and outline some new research challenges. Index Terms: speech-based interfaces, dialogue systems 1.

This thesis examines an important issue in spoken word recognition; how the perceptual system segments connected speech into lexical units or words. Research on this topic has investigated the role of different sources of information in dividing up the speech stream: acoustic cues in the speech signal, statistical regularities in the structure of the language or through the identification of individual lexical items. This research focuses on cases in which the location of word boundaries may be ambiguous by one or more of these segmentation mechanisms using words embedded at the onset of longer words (such as cap in captain). The ambiguities proposed for onset-embedded words have motivated accounts of segmentation based on competition between alternative parses of speech into words. In these accounts, the recognition of embedded words is delayed until after their offset when subsequent input rules out longer competitors. In this thesis it is demonstrated that training a simple recurrent network to activate a representation of all the words in a sequence allows a connectionist network to learn the appropriate delay to allow the identification of onset-embedded words without requiring directly implemented competition between words. Both lexical competition and recurrent network models assume ambiguity between onset-embedded words and equivalent syllables in longer competitors. Acoustic analysis carried out in this thesis confirms the presence of reliable acoustic differences between syllables in short and long words. A series of experiments using gating and cross-modal priming suggest that the perceptual system uses these acoustic differences to discriminate embedded words from the onset of longer competitors and that match or mismatch with longer competitors may be less important for the identification of onset-embedded words. These results are interpreted within a revised version of the recurrent network model, incorporating input representing the acoustic differences between syllables in short and long words.

All students of cognitive development agree that the central questions in development are 1) specifying the initial state of a human infant, 2) specifying the final state of development for a human adult, and 3) specifying how to get from the initial state to the final state. Then academic disputes ensue. Cognitive developmental psychologists are roughly divided into two camps: those who are more or less nativists and those who are more or less empiricists. Some psychologists do not like these terms, and some alternatives are “those who believe in innate knowledge ” and “those who believe in learning, ” or “those who believed in initial conceptual knowledge ” and “those who believe in initial perceptual capabilities. ” This division is also correlated with whether a researcher believes in domain specificity or not: nativists tend to argue for domain-specific knowledge (even at the beginning of development) and domain-specific learning mechanisms; empiricists tend to argue for domain-general learning mechanisms that may result in domain-specific knowledge some years into development (for some representative explications of these views, see Carey &

This paper presents an incremental probabilistic learner that models the acquistion of syntax and semantics from a corpus of child-directed utterances paired with possible representations of their meanings. These meaning representations approximate the contextual input available to the child; they do not specify the meanings of individual words or syntactic derivations. The learner then has to infer the meanings and syntactic properties of the words in the input along with a parsing model. We use the CCG grammatical framework and train a non-parametric Bayesian model of parse structure with online variational Bayesian expectation maximization. When tested on utterances from the CHILDES corpus, our learner outperforms a state-of-the-art semantic parser. In addition, it models such aspects of child acquisition as “fast mapping,” while also countering previous criticisms of statistical syntactic learners. 1

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depiction of how an avalanche encodes serial order.