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Situation-Based Multiparticipant Chat Summarization: a Concept, an Exploration-Annotation Tool and an Example Collection ...
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42 |
Don't Let Discourse Confine Your Model: Sequence Perturbations for Improved Event Language Models ...
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43 |
Boundary Detection with BERT for Span-level Emotion Cause Analysis ...
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44 |
TexSmart: A System for Enhanced Natural Language Understanding ...
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AND does not mean OR: Using Formal Languages to Study Language Models’ Representations ...
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Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images ...
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Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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48 |
Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search ...
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Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition ...
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51 |
VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes ...
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Attention-based Contextual Language Model Adaptation for Speech Recognition ...
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53 |
Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech ...
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ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning ...
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LUX (Linguistic aspects Under eXamination): Discourse Analysis for Automatic Fake News Classification ...
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56 |
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions ...
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58 |
SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images ...
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Abstract:
We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems. The task focused on memes and had three subtasks: (i) detecting the techniques in the text, (ii) detecting the text spans where the techniques are used, and (iii) detecting techniques in the entire meme, i.e., both in the text and in the image. It was a popular task, attracting 71 registrations, and 22 teams that eventually made an official submission on the test set. The evaluation results for the third subtask confirmed the importance of both modalities, the text and the image. Moreover, some teams reported benefits when not just combining the two modalities, e.g., by using early or late fusion, but rather modeling the interaction between them in a joint model. ...
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Keyword:
Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
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URL: https://dx.doi.org/10.48448/xb4g-cf90 https://underline.io/lecture/30026-semeval-2021-task-6-detection-of-persuasion-techniques-in-texts-and-images
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59 |
A DQN-based Approach to Finding Precise Evidences for Fact Verification ...
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Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event ...
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