Papers by Tim Klinger
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning (2024.eacl-long)
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Kinjal Basu, Keerthiram Murugesan, Subhajit Chaudhury, Murray Campbell, Kartik Talamadupula, Tim Klinger
| Challenge: | Text-based games (TBGs) combine natural language understanding with reasoning. |
| Approach: | They propose an exploration-guided reasoning agent for textual reinforcement learning that integrates natural language with reasoning. |
| Outcome: | The proposed agent outperforms baseline agents on TWG and TWC games. |
Recursive Routing Networks: Learning to Compose Modules for Language Understanding (N19-1)
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Ignacio Cases, Clemens Rosenbaum, Matthew Riemer, Atticus Geiger, Tim Klinger, Alex Tamkin, Olivia Li, Sandhini Agarwal, Joshua D. Greene, Dan Jurafsky, Christopher Potts, Lauri Karttunen
| Challenge: | Recursive Routing Networks are modular, adaptable models that learn effectively in diverse environments. |
| Approach: | They propose to apply Recursive Routing Networks (RRNs) to natural language understanding by integrating them into existing architectures and recurrent network hidden layers. |
| Outcome: | The proposed model optimizes the parameters of the functions and the meta-learner decision-making component for routing inputs through those functions. |
SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling (P18-4)
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| Challenge: | SANTO is an annotation tool designed for complex relation extraction tasks . a subset of information extraction tasks can be typed n-ary relation extraction or slot filling . |
| Approach: | They propose a domain-adaptive annotation tool for complex slot filling tasks . SANTO enables fast and clearly structured annotation for multiple users in parallel . |
| Outcome: | The proposed tool can be used for slot filling tasks and import and export procedures of standard formats enable interoperability with external sources and tools. |
Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing (2023.acl-long)
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Maxwell Crouse, Pavan Kapanipathi, Subhajit Chaudhury, Tahira Naseem, Ramon Fernandez Astudillo, Achille Fokoue, Tim Klinger
| Challenge: | Compositional generalization is a key feature of human intelligence and has been identified as a major point of weakness in neural methods for semantic parsing. |
| Approach: | They propose a neural parsing generation method that constructs logical forms from the bottom up, beginning from the logical form’s leaves. |
| Outcome: | The proposed method outperforms general-purpose parsers on a CFQ dataset and two other Text-to-SQL datasets while also being competitive with parser that have been tailored to each task. |