DE eng

Search in the Catalogues and Directories

Page: 1 2 3 4
Hits 1 – 20 of 64

1
Fairlex: A multilingual benchmark for evaluating fairness in legal text processing ...
BASE
Show details
2
Fairlex: A multilingual benchmark for evaluating fairness in legal text processing ...
BASE
Show details
3
UK-LEX Dataset - Part of Chalkidis and Søgaard (2022) ...
Chalkidis, Ilias; Søgaard, Anders. - : Zenodo, 2022
BASE
Show details
4
UK-LEX Dataset - Part of Chalkidis and Søgaard (2022) ...
Chalkidis, Ilias; Søgaard, Anders. - : Zenodo, 2022
BASE
Show details
5
FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing ...
BASE
Show details
6
Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks ...
BASE
Show details
7
Challenges and Strategies in Cross-Cultural NLP ...
BASE
Show details
8
Factual Consistency of Multilingual Pretrained Language Models ...
BASE
Show details
9
Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning ...
BASE
Show details
10
How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronouns ...
BASE
Show details
11
Replicating and Extending "Because Their Treebanks Leak": Graph Isomorphism, Covariants, and Parser Performance ...
BASE
Show details
12
The Impact of Positional Encodings on Multilingual Compression ...
BASE
Show details
13
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ...
BASE
Show details
14
Evaluation of Summarization Systems across Gender, Age, and Race ...
BASE
Show details
15
Locke's Holiday: Belief Bias in Machine Reading ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.649/ Abstract: I highlight a simple failure mode of state-of- the-art machine reading systems: when contexts do not align with commonly shared beliefs. For example, machine reading systems fail to answer "What did Elizabeth want?" correctly in the context of ’My kingdom for a cough drop, cried Queen Elizabeth.’ Biased by co-occurrence statistics in the training data of pretrained language models, systems predict 'my kingdom', rather than 'a cough drop'. I argue such biases are analogous to human belief biases and present a carefully designed challenge dataset for English machine reading, called AUTO-LOCKE, to quantify such effects. Evaluations of machine reading systems on AUTO-LOCKE show the pervasiveness of be- lief bias in machine reading. ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
URL: https://underline.io/lecture/37792-locke's-holiday-belief-bias-in-machine-reading
https://dx.doi.org/10.48448/t4tt-2j38
BASE
Hide details
16
Dynamic Forecasting of Conversation Derailment ...
BASE
Show details
17
Replicating and Extending ``Because Their Treebanks Leak'': Graph Isomorphism, Covariants, and Parser Performance ...
BASE
Show details
18
Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color ...
BASE
Show details
19
Spurious Correlations in Cross-Topic Argument Mining ...
BASE
Show details
20
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ...
BASE
Show details

Page: 1 2 3 4

Catalogues
1
0
6
0
1
0
0
Bibliographies
7
0
0
0
0
0
2
0
1
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
51
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern