arXiv:2007.12076
공식 코드 · Papers with Code
HCMS at SemEval-2020 Task 9: A Neural Approach to Sentiment Analysis for Code-Mixed Texts
원문 초록
Problems involving code-mixed language are often plagued by a lack of resources and an absence of materials to perform sophisticated transfer learning with. In this paper we describe our submission to the Sentimix Hindi-English task involving sentiment classification of code-mixed texts, and with an F1 score of 67.1%, we demonstrate that simple convolution and attention may well produce reasonable results.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Reliable Decision Support with LLMs: A Framework for Evaluating Consistency in Binary Text Classification Applications
MAGE: Multi-Head Attention Guided Embeddings for Low Resource Sentiment Classification
Text Classification in the LLM Era - Where do we stand?
A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations
Divide (Text) and Conquer (Sentiment): Improved Sentiment Classification by Constituent Conflict Resolution
A Simple Ensemble Strategy for LLM Inference: Towards More Stable Text Classification
토론
이 논문과 연결된 포럼 글이 아직 없습니다.