Learning High-Level Planning from Text

By S. Branavan, Nate Kushman, Tao Lei, Regina Barzilay. 2012

75 citations recorded by the source corpus.

Subfields: Natural Language Processing, LLM Agents & Tool Use

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Citations in the corpus

Semantic Parsing on Freebase from Question-Answer Pairs (2013, methodology): “Rather than using head-modifier information from dependency trees (Branavan et al., 2012; Krishnamurthy and Mitchell, 2012; Cai and Yates, 2013; Poon, 2013), we can learn the appropriate relationships tailored for downstream accuracy.”
Learning to Solve Arithmetic Word Problems with Verb Categorization (2014, background): “Our work is also closely related to the grounded language acquisition research (Snyder and Barzi-lay, 2007; Branavan et al., 2009; Branavan et al., 2012; Vogel and Jurafsky, 2010; Chen et al., 2010; Hajishirzi et al., 2011; Chambers and Jurafsky, 2009; Liang et al., 2009; Bordes et al., 2010) where…”
When Waiting is not an Option : Learning Options with a Deliberation Cost (2017, background): “Options offer a mechanism for communicating intents to and from an agent (Branavan et al. 2012; Andreas et al. 2017) more efﬁciently, by compressing the information into a simpler form, sending only the identiﬁer of the options and not the details themselves.”
Solving Geometry Problems: Combining Text and Diagram Interpretation (2015, background): “Our work is part of grounded language acquisition research (Branavan et al., 2012; Vogel and Jurafsky, 2010; Chen et al., 2010; Hajishirzi et al., 2011; Liang et al., 2009; Koncel-Kedziorski et al., 2014; Bordes et al., 2010; Kim and Mooney, 2013; Angeli and Manning, 2014; Hixon et al., 2015;…”
Cooking with Semantics (2014, methodology): “For example, we can prefer a parse that results in a ﬁnal state in which all the ingredients 4 There has been prior work on learning world models from text, see e.g., (Sil and Yates, 2011; Branavan et al., 2012). have been consumed, and the meal is prepared.”
PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards (2020, methodology): “Branavan et al. (2012b) use a game manual to speed up learning, wherein the most rel- evant sentence from the manual is found for the current state using a log-linear model, and features are extracted from the sentence to augment the state representation.”
An Introduction to Deep Reinforcement Learning (2018, unknown): “platform (Tessler et al., 2017; Matiisen et al., 2017; Branavan et al., 2012; Oh et al., 2016).”
Back to the Blocks World: Learning New Actions through Situated Human-Robot Dialogue (2014, background): “Most previous work on following human instructions are based on supervised learning (Kol-lar et al., 2010; Tellex et al., 2011; Chen et al., 2010) or reinforcement learning (Branavan et al., 2012; Branavan et al., 2010).”
RTFM: Generalising to Novel Environment Dynamics via Reading (2019, background): “, 2019), or the dynamics of the environment vary and are presented in language for some fixed goal (Branavan et al., 2012).”