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KOAS: Korean Text Offensiveness Analysis System ...
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Minimal Supervision for Morphological Inflection ...
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3
Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent ...
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4
Softmax Tree: An Accurate, Fast Classifier When the Number of Classes Is Large ...
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5
Multivalent Entailment Graphs for Question Answering ...
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GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation ...
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7
RuleBERT: Teaching Soft Rules to Pre-Trained Language Models ...
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8
Implicit Premise Generation with Discourse-aware Commonsense Knowledge Models ...
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9
On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings ...
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10
Is Everything in Order? A Simple Way to Order Sentences ...
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11
Cross-Domain Label-Adaptive Stance Detection ...
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12
Enhanced Language Representation with Label Knowledge for Span Extraction ...
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13
The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers ...
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14
VeeAlign: Multifaceted Context Representation Using Dual Attention for Ontology Alignment ...
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15
Shortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning ...
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16
On Classifying whether Two Texts are on the Same Side of an Argument ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.795/ Abstract: To ease the difficulty of argument stance classification, the task of same side stance classification (S3C) has been proposed. In contrast to actual stance classification, which requires a substantial amount of domain knowledge to identify whether an argument is in favor or against a certain issue, it is argued that, for S3C, only argument similarity within stances needs to be learned to successfully solve the task. We evaluate several transformer-based approaches on the dataset of the recent S3C shared task, followed by an in-depth evaluation and error analysis of our model and the task's hypothesis. We show that, although we achieve state-of-the-art results, our model fails to generalize both within as well as across topics and domains when adjusting the sampling strategy of the training and test set to a more adversarial scenario. Our evaluation shows that current state-of-the-art approaches cannot determine same side stance by ...
Keyword: Language Models; Natural Language Processing; Semantic Evaluation; Sociolinguistics
URL: https://underline.io/lecture/37386-on-classifying-whether-two-texts-are-on-the-same-side-of-an-argument
https://dx.doi.org/10.48448/wmx4-0145
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17
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP ...
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18
MTAdam: Automatic Balancing of Multiple Training Loss Terms ...
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19
Types of Out-of-Distribution Texts and How to Detect Them ...
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20
Asking It All: Generating Contextualized Questions for any Semantic Role ...
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