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1
Minimal Supervision for Morphological Inflection ...
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2
Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent ...
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3
Softmax Tree: An Accurate, Fast Classifier When the Number of Classes Is Large ...
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4
Multivalent Entailment Graphs for Question Answering ...
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5
GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation ...
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6
RuleBERT: Teaching Soft Rules to Pre-Trained Language Models ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.110/ Abstract: While pre-trained language models (PLMs) are the go-to solution to tackle many natural language processing problems, they are still very limited in their ability to capture and to use common-sense knowledge. In fact, even if information is available in the form of approximate (soft) logical rules, it is not clear how to transfer it to a PLM in order to improve its performance for deductive reasoning tasks. Here, we aim to bridge this gap by teaching PLMs how to reason with soft Horn rules. We introduce a classification task where, given facts and soft rules, the PLM should return a prediction with a probability for a given hypothesis. We release the first dataset for this task, and we propose a revised loss function that enables the PLM to learn how to predict precise probabilities for the task. Our evaluation results show that the resulting fine-tuned models achieve very high performance, even on logical rules that were unseen at ...
Keyword: Language Models; Natural Language Processing; Semantic Evaluation; Sociolinguistics
URL: https://dx.doi.org/10.48448/j90c-eg06
https://underline.io/lecture/37622-rulebert-teaching-soft-rules-to-pre-trained-language-models
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7
Implicit Premise Generation with Discourse-aware Commonsense Knowledge Models ...
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8
On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings ...
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9
Is Everything in Order? A Simple Way to Order Sentences ...
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10
Cross-Domain Label-Adaptive Stance Detection ...
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11
Enhanced Language Representation with Label Knowledge for Span Extraction ...
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12
The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers ...
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13
VeeAlign: Multifaceted Context Representation Using Dual Attention for Ontology Alignment ...
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14
Shortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning ...
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15
On Classifying whether Two Texts are on the Same Side of an Argument ...
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16
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP ...
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17
MTAdam: Automatic Balancing of Multiple Training Loss Terms ...
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18
Types of Out-of-Distribution Texts and How to Detect Them ...
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19
Asking It All: Generating Contextualized Questions for any Semantic Role ...
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20
Competency Problems: On Finding and Removing Artifacts in Language Data ...
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