Michael C. Frank

Also published as: Michael Frank


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The Emergence of the Shape Bias Results from Communicative Efficiency
Eva Portelance | Michael C. Frank | Dan Jurafsky | Alessandro Sordoni | Romain Laroche
Proceedings of the 25th Conference on Computational Natural Language Learning

By the age of two, children tend to assume that new word categories are based on objects’ shape, rather than their color or texture; this assumption is called the shape bias. They are thought to learn this bias by observing that their caregiver’s language is biased towards shape based categories. This presents a chicken and egg problem: if the shape bias must be present in the language in order for children to learn it, how did it arise in language in the first place? In this paper, we propose that communicative efficiency explains both how the shape bias emerged and why it persists across generations. We model this process with neural emergent language agents that learn to communicate about raw pixelated images. First, we show that the shape bias emerges as a result of efficient communication strategies employed by agents. Second, we show that pressure brought on by communicative need is also necessary for it to persist across generations; simply having a shape bias in an agent’s input language is insufficient. These results suggest that, over and above the operation of other learning strategies, the shape bias in human learners may emerge and be sustained by communicative pressures.


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The Development of Abstract Concepts in Children’s Early Lexical Networks
Abdellah Fourtassi | Isaac Scheinfeld | Michael Frank
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

How do children learn abstract concepts such as animal vs. artifact? Previous research has suggested that such concepts can partly be derived using cues from the language children hear around them. Following this suggestion, we propose a model where we represent the children’ developing lexicon as an evolving network. The nodes of this network are based on vocabulary knowledge as reported by parents, and the edges between pairs of nodes are based on the probability of their co-occurrence in a corpus of child-directed speech. We found that several abstract categories can be identified as the dense regions in such networks. In addition, our simulations suggest that these categories develop simultaneously, rather than sequentially, thanks to the children’s word learning trajectory which favors the exploration of the global conceptual space.


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Alignment at Work: Using Language to Distinguish the Internalization and Self-Regulation Components of Cultural Fit in Organizations
Gabriel Doyle | Amir Goldberg | Sameer Srivastava | Michael Frank
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Cultural fit is widely believed to affect the success of individuals and the groups to which they belong. Yet it remains an elusive, poorly measured construct. Recent research draws on computational linguistics to measure cultural fit but overlooks asymmetries in cultural adaptation. By contrast, we develop a directed, dynamic measure of cultural fit based on linguistic alignment, which estimates the influence of one person’s word use on another’s and distinguishes between two enculturation mechanisms: internalization and self-regulation. We use this measure to trace employees’ enculturation trajectories over a large, multi-year corpus of corporate emails and find that patterns of alignment in the first six months of employment are predictive of individuals’ downstream outcomes, especially involuntary exit. Further predictive analyses suggest referential alignment plays an overlooked role in linguistic alignment.


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Investigating the Sources of Linguistic Alignment in Conversation
Gabriel Doyle | Michael C. Frank
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Shared common ground influences information density in microblog texts
Gabriel Doyle | Michael Frank
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Audience size and contextual effects on information density in Twitter conversations
Gabriel Doyle | Michael Frank
Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics


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Parsing entire discourses as very long strings: Capturing topic continuity in grounded language learning
Minh-Thang Luong | Michael C. Frank | Mark Johnson
Transactions of the Association for Computational Linguistics, Volume 1

Grounded language learning, the task of mapping from natural language to a representation of meaning, has attracted more and more interest in recent years. In most work on this topic, however, utterances in a conversation are treated independently and discourse structure information is largely ignored. In the context of language acquisition, this independence assumption discards cues that are important to the learner, e.g., the fact that consecutive utterances are likely to share the same referent (Frank et al., 2013). The current paper describes an approach to the problem of simultaneously modeling grounded language at the sentence and discourse levels. We combine ideas from parsing and grammar induction to produce a parser that can handle long input strings with thousands of tokens, creating parse trees that represent full discourses. By casting grounded language learning as a grammatical inference task, we use our parser to extend the work of Johnson et al. (2012), investigating the importance of discourse continuity in children’s language acquisition and its interaction with social cues. Our model boosts performance in a language acquisition task and yields good discourse segmentations compared with human annotators.


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Exploiting Social Information in Grounded Language Learning via Grammatical Reduction
Mark Johnson | Katherine Demuth | Michael Frank
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Learning Words and Their Meanings from Unsegmented Child-directed Speech
Bevan K. Jones | Mark Johnson | Michael C. Frank
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics


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Modeling Word Learning As Communicative Inference
Michael C. Frank
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)