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The 2021 Conference on Empirical Methods in Natural Language Processing 2021 (1.029)
Cotterell, Ryan (8)
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Hits 121 – 140 of 1.029
121
End-to-end style-conditioned poetry generation: What does it take to learn from examples alone? ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Eger, Steffen
. - : Underline Science Inc., 2021
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122
Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Keller, Frank
;
Wilmot, David
. - : Underline Science Inc., 2021
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123
A Thorough Evaluation of Task-Specific Pretraining for Summarization ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Maynez, Joshua
;
Narayan, Shashi
. - : Underline Science Inc., 2021
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124
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Esplà-Gomis, Miquel
;
Pérez-Ortiz, Juan Antonio
. - : Underline Science Inc., 2021
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125
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Lewis, Patrick
. - : Underline Science Inc., 2021
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126
Good-Enough Example Extrapolation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Wei, Jason
. - : Underline Science Inc., 2021
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127
To what extent do human explanations of model behavior align with actual model behavior? ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Prasad, Grusha
. - : Underline Science Inc., 2021
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128
Sequence Length is a Domain: Length-based Overfitting in Transformer Models ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Bojar, Ondřej
;
Variš, Dušan
. - : Underline Science Inc., 2021
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129
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Ju, Yiming
;
Zhang, Yuanzhe
. - : Underline Science Inc., 2021
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130
Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Mercatali, Giangiacomo
. - : Underline Science Inc., 2021
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131
Effective Sequence-to-Sequence Dialogue State Tracking ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Cao, Yuan
;
Mahdieh, Mahdis
. - : Underline Science Inc., 2021
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132
An Investigation into the Contribution of Locally Aggregated Descriptors to Figurative Language Identification ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Indrakshi
;
Banerjee, Ritwik
. - : Underline Science Inc., 2021
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133
Solving Aspect Category Sentiment Analysis as a Text Generation Task ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Liu, Jian
. - : Underline Science Inc., 2021
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134
Discourse-Driven Integrated Dialogue Development Environment for Open-Domain Dialogue Systems ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Dmitry
;
Burtsev, Mikhail
. - : Underline Science Inc., 2021
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135
Context or No Context? A preliminary exploration of human-in-the-loop approach for Incremental Temporal Summarization in meetings ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Saurav
;
Beckage, Nicole
. - : Underline Science Inc., 2021
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136
Learning Data Augmentation Schedules for Natural Language Processing ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Irena
;
Chopard, Daphné
. - : Underline Science Inc., 2021
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137
Locke's Holiday: Belief Bias in Machine Reading ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Søgaard, Anders
. - : Underline Science Inc., 2021
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
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138
Searching for More Efficient Dynamic Programs ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
. - : Underline Science Inc., 2021
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139
Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Shi, Qi
. - : Underline Science Inc., 2021
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140
Improving Synonym Recommendation Using Sentence Context ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., William
;
Arendt, Dustin
. - : Underline Science Inc., 2021
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