Fast Online Lexicon Learning for Grounded Language Acquisition

By David L. Chen. 2012

38 citations recorded by the source corpus.

Subfields: Natural Language Processing, Reinforcement Learning

How researchers see this paper

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

In Natural Language Processing

In Reinforcement Learning

Citations in the corpus

Semantic Parsing on Freebase from Question-Answer Pairs (2013, background): “, 2011), following navigational instructions (Chen, 2012; Artzi and Zettlemoyer, 2013), and interacting in the real world via perception (Matuszek et al.”
Learning to Automatically Solve Algebra Word Problems (2014, background): “, 2013), dialog systems (Artzi and Zettlemoyer, 2011), robot instruction (Chen and Mooney, 2011; Chen, 2012; Kim and Mooney, 2012; Matuszek et al., 2012; Artzi and Zettlemoyer, 2013), and program executions (Kushman and Barzilay, 2013; Lei et al.”
Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions (2013, methodology): “In contrast to experiments on the Chen and Mooney (2011) cor-”
Speaker-Follower Models for Vision-and-Language Navigation (2018, background): “Systems that learn to carry out natural language instructions in an interactive environment include approaches based on intermediate structured and executable representations of language [50, 9, 4, 28, 20] and approaches that map directly from language and world state observations to actions [7, 2, ”
Learning Language Games through Interaction (2016, methodology): “While online gradient descent is frequently used, for example in semantic parsing (Zettlemoyer and Collins, 2007; Chen, 2012), we using it in a truly online setting, taking one pass over the data and measuring online accuracy (Cesa-Bianchi and Lugosi, 2006).”
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences (2015, background): “Previous work in this domain [6, 5, 15, 16, 1, 2] usually requires specialized knowledge and resources like semantic”
Measuring Compositionality in Representation Learning (2019, background): “Algorithms for learning grammars and lexicons from data are a mainstay of semantic parsing approaches to language understanding problems like question answering and instruction following (Zettlemoyer & Collins, 2005; Chen, 2012; Artzi et al., 2014).”
Broad-coverage CCG Semantic Parsing with AMR (2015, methodology): “Previous CCG induction techniques have either used hand-engineered lexical templates (e.g., Zettlemoyer and Collins, 2005) or learned templates from the data directly (e.g., Kwiatkowski et al., 2010, 2012).”
Bringing Machine Learning and Compositional Semantics Together (2015, background): “2010, 2011), following navigational instructions (Vogel & Jurafsky 2010; Chen 2012; Artzi & Zettlemoyer 2013), implementing dialogue systems (Artzi & Zettlemoyer 2011),”
Learning to Parse Natural Language Commands to a Robot Control System (2012, background): “Our work is most similar to that of Chen & Mooney [7, 6], who perform parser learning over a body of route instructions through a complex indoor environment containing objects and landmarks with no prior linguistic knowledge.”
Simpler Context-Dependent Logical Forms via Model Projections (2016, background): “In general, such systems rely on a lexicon, which can be hand-engineered, extracted (Cai and Yates, 2013; Berant et al., 2013), or automatically learned from annotated logical forms (Kwiatkowski et al., 2010; Chen, 2012).”