CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection

• URL: http://arxiv.org/abs/2008.09859v1
• Date: Sat, 22 Aug 2020 15:51:16 GMT
• Title: CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection
• Authors: Verena Blaschke, Maxim Korniyenko, Sam Tureski
• Abstract summary: We use a bi-LSTM architecture in the Span Identification subtask and train a complex ensemble model for the Technique Classification subtask. Our systems achieve a rank of 8 out of 35 teams in the SI subtask and 8 out of 31 teams in the TC subtask.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes our participation in the SemEval-2020 task Detection of Propaganda Techniques in News Articles. We participate in both subtasks: Span Identification (SI) and Technique Classification (TC). We use a bi-LSTM architecture in the SI subtask and train a complex ensemble model for the TC subtask. Our architectures are built using embeddings from BERT in combination with additional lexical features and extensive label post-processing. Our systems achieve a rank of 8 out of 35 teams in the SI subtask (F1-score: 43.86%) and 8 out of 31 teams in the TC subtask (F1-score: 57.37%).

Related papers

• SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection [68.858931667807]
  Subtask A is a binary classification task determining whether a text is written by a human or generated by a machine. Subtask B is to detect the exact source of a text, discerning whether it is written by a human or generated by a specific LLM. Subtask C aims to identify the changing point within a text, at which the authorship transitions from human to machine.
  arXiv  Detail & Related papers  (2024-04-22T13:56:07Z)
• LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning [122.47938710284784]
  We propose a novel framework for learning dynamic subtask assignment (LDSA) in cooperative MARL. To reasonably assign agents to different subtasks, we propose an ability-based subtask selection strategy. We show that LDSA learns reasonable and effective subtask assignment for better collaboration.
  arXiv  Detail & Related papers  (2022-05-05T10:46:16Z)
• Combining Modular Skills in Multitask Learning [149.8001096811708]
  A modular design encourages neural models to disentangle and recombine different facets of knowledge to generalise more systematically to new tasks. In this work, we assume each task is associated with a subset of latent discrete skills from a (potentially small) inventory. We find that the modular design of a network significantly increases sample efficiency in reinforcement learning and few-shot generalisation in supervised learning.
  arXiv  Detail & Related papers  (2022-02-28T16:07:19Z)
• SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning [47.49596196559958]
  This paper introduces the SemEval-2021 shared task 4: Reading of Abstract Meaning (ReCAM) Given a passage and the corresponding question, a participating system is expected to choose the correct answer from five candidates of abstract concepts. Subtask 1 aims to evaluate how well a system can model concepts that cannot be directly perceived in the physical world. Subtask 2 focuses on models' ability in comprehending nonspecific concepts located high in a hypernym hierarchy. Subtask 3 aims to provide some insights into models' generalizability over the two types of abstractness.
  arXiv  Detail & Related papers  (2021-05-31T11:04:17Z)
• TechTexC: Classification of Technical Texts using Convolution and Bidirectional Long Short Term Memory Network [0.0]
  A classification system (called 'TechTexC') is developed to perform the classification task using three techniques. Results show that CNN with BiLSTM model outperforms the other techniques concerning task-1 of sub-tasks (a, b, c and g) and task-2a. In the case of test set, the combined CNN with BiLSTM approach achieved that higher accuracy for the subtasks 1a (70.76%), 1b (79.97%), 1c (65.45%), 1g (49.23%) and 2a (70.14%)
  arXiv  Detail & Related papers  (2020-12-21T15:22:47Z)
• Solomon at SemEval-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles [0.3232625980782302]
  This paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 "Detection of Propaganda Techniques in News Articles" We used RoBERTa based transformer architecture for fine-tuning on the propaganda dataset. Compared to the other participating systems, our submission is ranked 4th on the leaderboard.
  arXiv  Detail & Related papers  (2020-09-16T05:00:40Z)
• YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles [5.352512345142247]
  This paper summarizes our studies on propaganda detection techniques for news articles in the SemEval-2020 task 11. We implement the GloVe word representation, the BERT pretraining model, and the LSTM model architecture to accomplish this task. Our method significantly outperforms the officially released baseline method, and the SI and TC subtasks rank 17th and 22nd, respectively, for the test set.
  arXiv  Detail & Related papers  (2020-08-24T02:42:12Z)
• syrapropa at SemEval-2020 Task 11: BERT-based Models Design For Propagandistic Technique and Span Detection [2.0051855303186046]
  We first build the model for Span Identification (SI) based on SpanBERT, and facilitate the detection by a deeper model and a sentence-level representation. We then develop a hybrid model for the Technique Classification (TC) The hybrid model is composed of three submodels including two BERT models with different training methods, and a feature-based Logistic Regression model.
  arXiv  Detail & Related papers  (2020-08-24T02:15:29Z)
• aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer Learning [22.90521056447551]
  We describe our system for SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. We developed ensemble models using RoBERTa-based neural architectures, additional CRF layers, transfer learning between the two subtasks, and advanced post-processing to handle the multi-label nature of the task.
  arXiv  Detail & Related papers  (2020-08-06T18:45:25Z)
• Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation [63.98724740606457]
  We present a joint effort of four groups, namely GT, USTC, Tencent, and UKE, to tackle Task 1 - Acoustic Scene Classification (ASC) in the DCASE 2020 Challenge. Task 1a focuses on ASC of audio signals recorded with multiple (real and simulated) devices into ten different fine-grained classes. Task 1b concerns with classification of data into three higher-level classes using low-complexity solutions.
  arXiv  Detail & Related papers  (2020-07-16T15:07:14Z)
• MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning [82.62433731378455]
  We show that tasks with high affinity at a certain scale are not guaranteed to retain this behaviour at other scales. We propose a novel architecture, namely MTI-Net, that builds upon this finding.
  arXiv  Detail & Related papers  (2020-01-19T21:02:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.