OCHADAI at SemEval-2022 Task 2: Adversarial Training for Multilingual Idiomaticity Detection
We propose a multilingual adversarial training model for determining whether a sentence contains an idiomatic expression. Given that a key challenge with this task is the limited size of annotated data, our model relies on pre-trained contextual representations from different multi-lingual state-of-the-art transformer-based language models (i.e., multilingual BERT and XLM-RoBERTa), and on adversarial training, a training method for further enhancing model generalization and robustness. Without relying on any human-crafted features, knowledge bases, or additional datasets other than the target datasets, our model achieved competitive results and ranked 6th place in SubTask A (zero-shot) setting and 15th place in SubTask A (one-shot) setting.
Code (0)
등록된 구현이 없습니다.
Tasks
SentenceTask 2Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
OCHADAI-KYOTO at SemEval-2021 Task 1: Enhancing Model Generalization and Robustness for Lexical Complexity Prediction
We propose an ensemble model for predicting the lexical complexity of words and multiword expressions (MWEs). The model receives as input a sentence with a target word or MWEand outputs its complexity score. Given that a…
Lexical Complexity PredictionMulti-Task LearningSentenceZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations
This paper presents our contribution to the SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding.We explore the impact of three different pre-trained multilingual language models in the SubTask…
Adversarial AttackContrastive LearningSentenceTask 2STUFIIT at SemEval-2019 Task 5: Multilingual Hate Speech Detection on Twitter with MUSE and ELMo Embeddings
We present a number of models used for hate speech detection for Semeval 2019 Task-5: Hateval. We evaluate the viability of multilingual learning for this task. We also experiment with adversarial learning as a means of …
Hate Speech DetectionWord Embeddingskk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification
Code switching is a linguistic phenomenon that may occur within a multilingual setting where speakers share more than one language. With the increasing communication between groups with different languages, this phenomen…
ClassificationGeneral ClassificationLanguage ModelingLanguage Modelling+3PAW 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