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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 59 1 Developing Conversational Data and Detection of Conversational Humor in Telugu ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Pétur O.; Jónsson, Haukur P.. - : Underline Science Inc., 2021 BASE Show details 2 Detecting Gender Bias using Explainability ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Krithika; Gupta, Gauri. - : Underline Science Inc., 2021 BASE Show details 3 Characterizing Test Anxiety on Social Media ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Julka, Esha; Kowalishin, Olivia. - : Underline Science Inc., 2021 BASE Show details 4 Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ong, Desmond; Suresh, Varsha. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.359/ Abstract: Fine-grained classification involves dealing with datasets with larger number of classes with subtle differences between them. Guiding the model to focus on differentiating dimensions between these commonly confusable classes is key to improving performance on fine-grained tasks. In this work, we analyse the contrastive fine-tuning of pre-trained language models on two fine-grained text classification tasks, emotion classification, and sentiment analysis. We adaptively embed class relationships into a contrastive objective function to help differently weigh the positives and negatives, and in particular, weighting closely confusable negatives more than less similar negative examples. We find that Label-aware Contrastive Loss outperforms previous contrastive methods, in the presence of larger number and/or more confusable classes, and helps models to produce output distributions that are more differentiated. ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Sentiment Analysis URL: https://dx.doi.org/10.48448/ywz3-2188 https://underline.io/lecture/38032-not-all-negatives-are-equal-label-aware-contrastive-loss-for-fine-grained-text-classification BASE Hide details 5 Open Aspect Target Sentiment Classification with Natural Language Prompts ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Birle, Ian; Chang, Haw-Shiuan. - : Underline Science Inc., 2021 BASE Show details 6 SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Maneriker, Pranav. - : Underline Science Inc., 2021 BASE Show details 7 Connecting Attributions and QA Model Behavior on Realistic Counterfactuals ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Durrett, Greg; Nair, Rohan. - : Underline Science Inc., 2021 BASE Show details 8 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 BASE Show details 9 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 BASE Show details 10 CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ke, Zixuan; Liu, Bing. - : Underline Science Inc., 2021 BASE Show details 11 Improving Multimodal fusion via Mutual Dependency Maximisation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Chapuis, Emile; Clavel, Chloé. - : Underline Science Inc., 2021 BASE Show details 12 Perceived and Intended Sarcasm Detection with Graph Attention Networks ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Flek, Lucie; Plepi, Joan. - : Underline Science Inc., 2021 BASE Show details 13 Does Commonsense help in detecting Sarcasm? ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Basu Roy Chowdhury, Somnath; Chaturvedi, Snigdha. - : Underline Science Inc., 2021 BASE Show details 14 Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Chen, Guimin; qin, han. - : Underline Science Inc., 2021 BASE Show details 15 How much coffee was consumed during EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Chandrasekaran, Arjun; Clark, Peter. - : Underline Science Inc., 2021 BASE Show details 16 LMSOC: An Approach for Socially Sensitive Pretraining ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Aria; Kulkarni, Vivek. - : Underline Science Inc., 2021 BASE Show details 17 Towards Label-Agnostic Emotion Embeddings ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Buechel, Sven; Hahn, Udo. - : Underline Science Inc., 2021 BASE Show details 18 The Effect of Round-Trip Translation on Fairness in Sentiment Analysis ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Christiansen, Jonathan Gabel; Gammelgaard, Mathias Lykke. - : Underline Science Inc., 2021 BASE Show details 19 Multimodal Phased Transformer for Sentiment Analysis ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cheng, Junyan; Fostiropoulos, Iordanis. - : Underline Science Inc., 2021 BASE Show details 20 MATE: Multi-view Attention for Table Transformer Efficiency ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cohen, William; Eisenschlos, Julian. - : Underline Science Inc., 2021 BASE Show details Page: 1 2 3

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