1 |
Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Bird's Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
How Good Is NLP?A Sober Look at NLP Tasks through the Lens of Social Impact ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding ...
|
|
|
|
Abstract:
When reading a literary piece, readers often make inferences about various characters’ roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU – a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension. ... : Findings of the Association for Computational Linguistics: EMNLP 2021 ...
|
|
URL: https://dx.doi.org/10.3929/ethz-b-000527301 http://hdl.handle.net/20.500.11850/527301
|
|
BASE
|
|
Hide details
|
|
12 |
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
|
|
|
|
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
|
|
BASE
|
|
Show details
|
|
16 |
How Good Is NLP?A Sober Look at NLP Tasks through the Lens of Social Impact
|
|
|
|
In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021)
|
|
BASE
|
|
Show details
|
|
17 |
Differentiable subset pruning of transformer heads
|
|
|
|
In: Transactions of the Association for Computational Linguistics, 9 (2021)
|
|
BASE
|
|
Show details
|
|
18 |
Scaling Within Document Coreference to Long Texts
|
|
|
|
In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021)
|
|
BASE
|
|
Show details
|
|
19 |
“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
|
|
|
|
In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
|
|
BASE
|
|
Show details
|
|
20 |
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations
|
|
|
|
In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (2021)
|
|
BASE
|
|
Show details
|
|
|
|