NoPropaganda at SemEval-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification

• URL: http://arxiv.org/abs/2007.12913v1
• Date: Sat, 25 Jul 2020 11:35:57 GMT
• Title: NoPropaganda at SemEval-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification
• Authors: Ilya Dimov, Vladislav Korzun and Ivan Smurov
• Abstract summary: This paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. We start with simple LSTM baselines and move to an autoregressive transformer decoder to predict long continuous propaganda spans for the first subtask. We also adopt an approach from relation extraction by enveloping spans mentioned above with special tokens for the second subtask of propaganda technique classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. We start with simple LSTM baselines and move to an autoregressive transformer decoder to predict long continuous propaganda spans for the first subtask. We also adopt an approach from relation extraction by enveloping spans mentioned above with special tokens for the second subtask of propaganda technique classification. Our models report an F-score of 44.6% and a micro-averaged F-score of 58.2% for those tasks accordingly.

Related papers

• Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions [58.69255121795761]
  We propose a novel Active Transfer Few-shot Instructions (ATF) approach which requires no fine-tuning. ATF leverages the internal linguistic knowledge of pre-trained language models (PLMs) to facilitate the transfer of information. We show that annotation of just a few target-domain samples via active learning can be beneficial for transfer, but the impact diminishes with more annotation effort.
  arXiv  Detail & Related papers  (2022-11-21T19:03:31Z)
• Alibaba-Translate China's Submission for WMT 2022 Metrics Shared Task [61.34108034582074]
  We build our system based on the core idea of UNITE (Unified Translation Evaluation) During the model pre-training phase, we first apply the pseudo-labeled data examples to continuously pre-train UNITE. During the fine-tuning phase, we use both Direct Assessment (DA) and Multidimensional Quality Metrics (MQM) data from past years' WMT competitions.
  arXiv  Detail & Related papers  (2022-10-18T08:51:25Z)
• PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation [89.0074567748505]
  We propose a new metric to accurately predict the prompt transferability (regarding (i)), and a novel PoT approach (namely PANDA) Our proposed metric works well to predict the prompt transferability; 2) our PANDA consistently outperforms the vanilla PoT approach by 2.3% average score (up to 24.1%) among all tasks and model sizes; 3) with our PANDA approach, prompt-tuning can achieve competitive and even better performance than model-tuning in various PLM scales scenarios.
  arXiv  Detail & Related papers  (2022-08-22T09:14:14Z)
• 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)
• SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles [0.6999740786886536]
  We present the results of SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. The task featured two subtasks: Span Identification and Technique Classification. For both subtasks, the best systems used pre-trained Transformers and ensembles.
  arXiv  Detail & Related papers  (2020-09-06T10:05:43Z)
• 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)
• DUTH at SemEval-2020 Task 11: BERT with Entity Mapping for Propaganda Classification [1.5469452301122173]
  This report describes the methods employed by the Democritus University of Thrace (DUTH) team for participating in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles.
  arXiv  Detail & Related papers  (2020-08-22T18:18:02Z)
• LTIatCMU at SemEval-2020 Task 11: Incorporating Multi-Level Features for Multi-Granular Propaganda Span Identification [70.1903083747775]
  This paper describes our submission for the task of Propaganda Span Identification in news articles. We introduce a BERT-BiLSTM based span-level propaganda classification model that identifies which token spans within the sentence are indicative of propaganda.
  arXiv  Detail & Related papers  (2020-08-11T16:14:47Z)
• SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media [50.29389719723529]
  We present the main findings and compare the results of SemEval-2020 Task 10, Emphasis Selection for Written Text in Visual Media. The goal of this shared task is to design automatic methods for emphasis selection. The analysis of systems submitted to the task indicates that BERT and RoBERTa were the most common choice of pre-trained models used.
  arXiv  Detail & Related papers  (2020-08-07T17:24:53Z)
• newsSweeper at SemEval-2020 Task 11: Context-Aware Rich Feature Representations For Propaganda Classification [2.0491741153610334]
  This paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles. We make use of pre-trained BERT language model enhanced with tagging techniques developed for the task of Named Entity Recognition. For the second subtask, we incorporate contextual features in a pre-trained RoBERTa model for the classification of propaganda techniques.
  arXiv  Detail & Related papers  (2020-07-21T14:06:59Z)
• Cross-media Structured Common Space for Multimedia Event Extraction [82.36301617438268]
  We introduce a new task, MultiMedia Event Extraction (M2E2), which aims to extract events and their arguments from multimedia documents. We propose a novel method, Weakly Aligned Structured Embedding (WASE), that encodes structured representations of semantic information into a common embedding space. By utilizing images, we extract 21.4% more event mentions than traditional text-only methods.
  arXiv  Detail & Related papers  (2020-05-05T20:21:53Z)

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.