Team QUST at SemEval-2023 Task 3: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting Online News Genre, Framing and Persuasion Techniques

• URL: http://arxiv.org/abs/2304.04190v1
• Date: Sun, 9 Apr 2023 08:14:01 GMT
• Title: Team QUST at SemEval-2023 Task 3: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting Online News Genre, Framing and Persuasion Techniques
• Authors: Ye Jiang
• Abstract summary: This paper describes the participation of team QUST in the SemEval2023 task 3. The monolingual models are first evaluated with the under-sampling of the majority classes. The pre-trained multilingual model is fine-tuned with a combination of the class weights and the sample weights.
• Score: 0.030458514384586396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes the participation of team QUST in the SemEval2023 task 3. The monolingual models are first evaluated with the under-sampling of the majority classes in the early stage of the task. Then, the pre-trained multilingual model is fine-tuned with a combination of the class weights and the sample weights. Two different fine-tuning strategies, the task-agnostic and the task-dependent, are further investigated. All experiments are conducted under the 10-fold cross-validation, the multilingual approaches are superior to the monolingual ones. The submitted system achieves the second best in Italian and Spanish (zero-shot) in subtask-1.

Related papers

• KInITVeraAI at SemEval-2023 Task 3: Simple yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection [0.0]
  This paper presents the best-performing solution to the SemEval 2023 Task 3 on the subtask 3 dedicated to persuasion techniques detection. Due to a high multilingual character of the input data and a large number of 23 predicted labels, we opted for fine-tuning pre-trained transformer-based language models.
  arXiv  Detail & Related papers  (2023-04-24T09:06:43Z)
• SheffieldVeraAI at SemEval-2023 Task 3: Mono and multilingual approaches for news genre, topic and persuasion technique classification [3.503844033591702]
  This paper describes our approach for SemEval-2023 Task 3: Detecting the category, the framing, and the persuasion techniques in online news in a multi-lingual setup.
  arXiv  Detail & Related papers  (2023-03-16T15:54:23Z)
• Hitachi at SemEval-2023 Task 3: Exploring Cross-lingual Multi-task Strategies for Genre and Framing Detection in Online News [10.435874177179764]
  This paper explains the participation of team Hitachi to SemEval-2023 Task 3 "Detecting the genre, the framing, and the persuasion techniques in online news in a multi-lingual setup" We investigated different cross-lingual and multi-task strategies for training the pretrained language models. We constructed ensemble models from the results and achieved the highest macro-averaged F1 scores in Italian and Russian genre categorization subtasks.
  arXiv  Detail & Related papers  (2023-03-03T09:12:55Z)
• Advancing Multilingual Pre-training: TRIP Triangular Document-level Pre-training for Multilingual Language Models [107.83158521848372]
  We present textbfTriangular Document-level textbfPre-training (textbfTRIP), which is the first in the field to accelerate the conventional monolingual and bilingual objectives into a trilingual objective with a novel method called Grafting. TRIP achieves several strong state-of-the-art (SOTA) scores on three multilingual document-level machine translation benchmarks and one cross-lingual abstractive summarization benchmark, including consistent improvements by up to 3.11 d-BLEU points and 8.9 ROUGE-L points.
  arXiv  Detail & Related papers  (2022-12-15T12:14:25Z)
• Multilingual Relation Classification via Efficient and Effective Prompting [9.119073318043952]
  We present the first work on prompt-based multilingual relation classification (RC) We introduce an efficient and effective method that constructs prompts from relation triples and involves only minimal translation for the class labels. We evaluate its performance in fully supervised, few-shot and zero-shot scenarios, and analyze its effectiveness across 14 languages.
  arXiv  Detail & Related papers  (2022-10-25T08:40:23Z)
• Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of Multilingual Language Models [73.11488464916668]
  This study investigates the dynamics of the multilingual pretraining process. We probe checkpoints taken from throughout XLM-R pretraining, using a suite of linguistic tasks. Our analysis shows that the model achieves high in-language performance early on, with lower-level linguistic skills acquired before more complex ones.
  arXiv  Detail & Related papers  (2022-05-24T03:35:00Z)
• Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation [80.16548523140025]
  We extend the vanilla pretrain-finetune pipeline with extra code-switching restore task to bridge the gap between the pretrain and finetune stages. Our approach could narrow the cross-lingual sentence representation distance and improve low-frequency word translation with trivial computational cost.
  arXiv  Detail & Related papers  (2022-04-16T16:08:38Z)
• Delving Deeper into Cross-lingual Visual Question Answering [115.16614806717341]
  We show that simple modifications to the standard training setup can substantially reduce the transfer gap to monolingual English performance. We analyze cross-lingual VQA across different question types of varying complexity for different multilingual multimodal Transformers.
  arXiv  Detail & Related papers  (2022-02-15T18:22:18Z)
• Contributions of Transformer Attention Heads in Multi- and Cross-lingual Tasks [9.913751245347429]
  We show that pruning a number of attention heads in a multi-lingual Transformer-based model has, in general, positive effects on its performance in cross-lingual and multi-lingual tasks. For comprehensiveness, we examine two pre-trained multi-lingual models, namely multi-lingual BERT (mBERT) and XLM-R, on three tasks across 9 languages each.
  arXiv  Detail & Related papers  (2021-08-18T20:17:46Z)
• Are Multilingual Models Effective in Code-Switching? [57.78477547424949]
  We study the effectiveness of multilingual language models to understand their capability and adaptability to the mixed-language setting. Our findings suggest that pre-trained multilingual models do not necessarily guarantee high-quality representations on code-switching.
  arXiv  Detail & Related papers  (2021-03-24T16:20:02Z)
• Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension [86.1617182312817]
  We propose two auxiliary tasks in the fine-tuning stage to create additional phrase boundary supervision. A mixed Machine Reading task, which translates the question or passage to other languages and builds cross-lingual question-passage pairs. A language-agnostic knowledge masking task by leveraging knowledge phrases mined from web.
  arXiv  Detail & Related papers  (2020-04-29T10:44:00Z)

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.