A Bayesian framework for cross-situational word-learning
For infants, early word learning is a chicken-and-egg problem. One way to learn a word is to observe that it co-occurs with a particular referent across different situations. Another way is to use the social context of an utterance to infer the intended referent of a word. Here we present a Bayesian...
| Main Authors: | , , |
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| Format: | Article |
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Neural Information Processing Systems Foundation
2017
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| Online Access: | http://hdl.handle.net/1721.1/112917 https://orcid.org/0000-0002-1925-2035 |
| _version_ | 1826211233423949824 |
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| author | Goodman, Noah Daniel Tenenbaum, Joshua B Frank, Michael C. |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Goodman, Noah Daniel Tenenbaum, Joshua B Frank, Michael C. |
| author_sort | Goodman, Noah Daniel |
| collection | MIT |
| description | For infants, early word learning is a chicken-and-egg problem. One way to learn a word is to observe that it co-occurs with a particular referent across different situations. Another way is to use the social context of an utterance to infer the intended referent of a word. Here we present a Bayesian model of cross-situational word learning, and an extension of this model that also learns which social cues are relevant to determining reference. We test our model on a small corpus of mother-infant interaction and find it performs better than competing models. Finally, we show that our model accounts for experimental phenomena including mutual exclusivity, fast-mapping, and generalization from social cues. |
| first_indexed | 2024-09-23T15:02:59Z |
| format | Article |
| id | mit-1721.1/112917 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T15:02:59Z |
| publishDate | 2017 |
| publisher | Neural Information Processing Systems Foundation |
| record_format | dspace |
| spelling | mit-1721.1/1129172022-09-29T12:18:11Z A Bayesian framework for cross-situational word-learning Goodman, Noah Daniel Tenenbaum, Joshua B Frank, Michael C. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Goodman, Noah Daniel Tenenbaum, Joshua B Frank, Michael C. For infants, early word learning is a chicken-and-egg problem. One way to learn a word is to observe that it co-occurs with a particular referent across different situations. Another way is to use the social context of an utterance to infer the intended referent of a word. Here we present a Bayesian model of cross-situational word learning, and an extension of this model that also learns which social cues are relevant to determining reference. We test our model on a small corpus of mother-infant interaction and find it performs better than competing models. Finally, we show that our model accounts for experimental phenomena including mutual exclusivity, fast-mapping, and generalization from social cues. 2017-12-21T14:46:33Z 2017-12-21T14:46:33Z 2007-12 2017-12-08T18:53:00Z Article http://purl.org/eprint/type/ConferencePaper 1049-5258 http://hdl.handle.net/1721.1/112917 Frank, Michael C., Noah D. Goodman, and Joshua B. Tenenbaum. "A Bayesian Framework for Cross-Situational Word-Learning." Advances in Neural Information Processing Systems 20 (NIPS 2007), Vancouver, British Columbia, Canada, 3-8 December, 2007. © 2007 Neural Information Processing Systems Foundation https://orcid.org/0000-0002-1925-2035 https://papers.nips.cc/paper/3165-a-bayesian-framework-for-cross-situational-word-learning Advances in Neural Information Processing Systems 20 (NIPS 2007) Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Neural Information Processing Systems Foundation Neural Information Processing Systems (NIPS) |
| spellingShingle | Goodman, Noah Daniel Tenenbaum, Joshua B Frank, Michael C. A Bayesian framework for cross-situational word-learning |
| title | A Bayesian framework for cross-situational word-learning |
| title_full | A Bayesian framework for cross-situational word-learning |
| title_fullStr | A Bayesian framework for cross-situational word-learning |
| title_full_unstemmed | A Bayesian framework for cross-situational word-learning |
| title_short | A Bayesian framework for cross-situational word-learning |
| title_sort | bayesian framework for cross situational word learning |
| url | http://hdl.handle.net/1721.1/112917 https://orcid.org/0000-0002-1925-2035 |
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