Bootstrapping language acquisition.

By Omri Abend, Tom Kwiatkowski, Nathaniel J. Smith, Sharon Goldwater, Mark Steedman. 2017

90 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, Cognitive Science & Psychology, Linguistics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Josh Tenenbaum
2. Chuang Gan
3. Jiajun Wu
4. Pushmeet Kohli
5. Jiayuan Mao
6. Lei Li
7. Yuning Jiang
8. Hao Wu

In Developmental and Educational Psychology

1. Jiajun Wu
2. Pushmeet Kohli
3. Josh Tenenbaum
4. Tomer D. Ullman
5. T. Griffiths
6. Noah D. Goodman
7. Laura Schulz
8. Michael D. Lee

In Cognitive Science & Psychology

1. Chuang Gan
2. Josh Tenenbaum
3. Jiajun Wu
4. Tomer D. Ullman
5. Brenden M. Lake
6. Michael D. Lee

In Linguistics

1. Josh Tenenbaum

Citations in the corpus

• Emergent Abilities of Large Language Models (2022, neutral): “Overall, more work is needed to tease apart what enables scale to unlock emergent abilities.”
• The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision (2019, background): “…able to inductively learn the correspondence between visual concepts and word semantics (e.g., spatial relations and referential expressions, Figure 1-II), and unravel compositional logic from complex questions assisted by the learned visual concepts (Figure 1-III, also see Abend et al. (2017)).”
• Like a Baby: Visually Situated Neural Language Acquisition (2018, unknown): “Computational cognitive models can account for bootstrapped learning of word meaning and syntax when language is paired with perceptual experience (Abend et al., 2017) and for the ability of children to rapidly acquire new words by inferring the referent from their physical environment (Alishahi et…”
• Word learning and the acquisition of syntactic-semantic overhypotheses (2018, unknown): “A productive line of work on the joint learning of lexical syntax and semantics has largely relied on one particular parsing formalism known as combinatory categorial grammar (Abend et al., 2017; Steedman, 1996).”
• Unified Visual-Semantic Embeddings: Bridging Vision and Language With Structured Meaning Representations (2019, background): “The alignments are formed at multiple levels: This short sentence can be decomposed into a rich set of semantic components [3]:”