Papers by Murray Campbell
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering (D19-58)
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| Challenge: | Existing work on open-domain multi-hop question answering relies on off-the-shelf information retrieval techniques to retrieve answer passages. |
| Approach: | They propose a new subproblem for open-domain multi-hop question answering . they aim to recognize the anchor from a set of start passages with a reading comprehension model . |
| Outcome: | The proposed method significantly improves the baseline method on the open-domain hotpotQA benchmark. |
JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions (2023.findings-acl)
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| Challenge: | Existing benchmarks focus on a single reasoning type and ask human annotators to write candidate statements related to the particular type of commonsense. |
| Approach: | They propose a new commonsense reasoning dataset based on human’s Interactive Fiction (IF) gameplaywalkthroughs. |
| Outcome: | The proposed dataset is challenging to previous machine reading models and large language models with a significant 20%performance gap compared to human experts. |
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. |
Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning (2020.emnlp-main)
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| Challenge: | Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. |
| Approach: | They propose to re-formulate IF game solving as Multi-Passage Reading Comprehension tasks using context-query attention mechanisms and structured prediction to efficiently generate and evaluate action outputs. |
| Outcome: | The proposed methods achieve high winning rates and low data requirements on the recent IF benchmark (Jericho) |
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations (2021.acl-short)
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Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Kartik Talamadupula, Mrinmaya Sachan, Murray Campbell
| Challenge: | Text-based games (TBGs) are useful benchmarks for evaluating progress in grounded language understanding and reinforcement learning (RL). |
| Approach: | They propose an agent that induces a graph representation of the game state and jointly grounds it with a commonsense knowledge from ConceptNet. |
| Outcome: | The proposed agent outperforms baseline agents in the proposed game . |