Hitachi at SemEval-2023 Task 3: Exploring Cross-lingual Multi-task Strategies for Genre and Framing Detection in Online News
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.'' Based on the multilingual, multi-task nature of the task and the low-resource setting, we investigated different cross-lingual and multi-task strategies for training the pretrained language models. Through extensive experiments, we found that (a) cross-lingual/multi-task training, and (b) collecting an external balanced dataset, can benefit the genre and framing detection. We constructed ensemble models from the results and achieved the highest macro-averaged F1 scores in Italian and Russian genre categorization subtasks.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Hitachi at SemEval-2022 Task 2: On the Effectiveness of Span-based Classification Approaches for Multilingual Idiomaticity Detection
In this paper, we describe our system for SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding. The task aims at detecting idiomaticity in an input sequence (Subtask A) and modeling representat…
ClassificationSentenceSentence EmbeddingSentence-Embedding+2Hitachi at SemEval-2020 Task 3: Exploring the Representation Spaces of Transformers for Human Sense Word Similarity
In this paper, we present our system for SemEval-2020 task 3, Predicting the (Graded) Effect of Context in Word Similarity. Due to the unsupervised nature of the task, we concentrated on inquiring about the similarity me…
Word SimilarityHitachi at SemEval-2016 Task 12: A Hybrid Approach for Temporal Information Extraction from Clinical Notes
Hitachi at SemEval-2020 Task 10: Emphasis Distribution Fusion on Fine-Tuned Language Models
This paper shows our system for SemEval-2020 task 10, Emphasis Selection for Written Text in Visual Media. Our strategy is two-fold. First, we propose fine-tuning many pre-trained language models, predicting an emphasis …
PAW at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation : Exploring Cross Lingual Transfer, Augmentations and Adversarial Training
We experiment with XLM RoBERTa for Word in Context Disambiguation in the Multi Lingual and Cross Lingual setting so as to develop a single model having knowledge about both settings. We solve the problem as a binary clas…
Binary ClassificationCross-Lingual TransferData AugmentationTask 2