| 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 ...
|
|
|
|
BASE
|
|
Show 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)
|
|
Abstract:
The principle of independent causal mechanisms (ICM) states that generative processes of real world data consist of independent modules which do not influence or inform each other. While this idea has led to fruitful developments in the field of causal inference, it is not widely-known in the NLP community. In this work, we argue that the causal direction of the data collection process bears nontrivial implications that can explain a number of published NLP findings, such as differences in semi-supervised learning (SSL) and domain adaptation (DA) performance across different settings. We categorize common NLP tasks according to their causal direction and empirically assay the validity of the ICM principle for text data using minimum description length. We conduct an extensive meta-analysis of over 100 published SSL and 30 DA studies, and find that the results are consistent with our expectations based on causal insights. This work presents the first attempt to analyze the ICM principle in NLP, and provides constructive suggestions for future modeling choices.
|
|
URL: https://doi.org/10.3929/ethz-b-000527298
https://hdl.handle.net/20.500.11850/527298
|
|
BASE
|
|
Hide 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
|
|
|
|
|
