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Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang ...
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Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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Bird's Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact ...
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How Good Is NLP?A Sober Look at NLP Tasks through the Lens of Social Impact ...
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“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding ...
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Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations ...
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Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations ...
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Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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How Good Is NLP?A Sober Look at NLP Tasks through the Lens of Social Impact
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In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021)
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Differentiable subset pruning of transformer heads
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In: Transactions of the Association for Computational Linguistics, 9 (2021)
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Scaling Within Document Coreference to Long Texts
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In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021)
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“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations
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In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (2021)
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Abstract:
Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL). Recent work has proposed the use of external knowledge to improve the efficiency of RL agents for TBGs. In this paper, we posit that to act efficiently in TBGs, an agent must be able to track the state of the game while retrieving and using relevant commonsense knowledge. Thus, we propose an agent for TBGs that induces a graph representation of the game state and jointly grounds it with a graph of commonsense knowledge from ConceptNet. This combination is achieved through bidirectional knowledge graph attention between the two symbolic representations. We show that agents that incorporate commonsense into the game state graph outperform baseline agents.
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URL: https://doi.org/10.3929/ethz-b-000507679 https://hdl.handle.net/20.500.11850/507679
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