arXiv:2205.13708
공식 코드 · Papers with Code
HiJoNLP at SemEval-2022 Task 2: Detecting Idiomaticity of Multiword Expressions using Multilingual Pretrained Language Models
원문 초록
This paper describes an approach to detect idiomaticity only from the contextualized representation of a MWE over multilingual pretrained language models. Our experiments find that larger models are usually more effective in idiomaticity detection. However, using a higher layer of the model may not guarantee a better performance. In multilingual scenarios, the convergence of different languages are not consistent and rich-resource languages have big advantages over other languages.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts
LLM-BABYBENCH: Understanding and Evaluating Grounded Planning and Reasoning in LLMs
1$^{st}$ Place Solution of WWW 2025 EReL@MIR Workshop Multimodal CTR Prediction Challenge
Feature Fusion Revisited: Multimodal CTR Prediction for MMCTR Challenge
Quadratic Interest Network for Multimodal Click-Through Rate Prediction
FinBERT-QA: Financial Question Answering with pre-trained BERT Language Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.