HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News Similarity
This paper describes our system designed for SemEval-2022 Task 8: Multilingual News Article Similarity. We proposed a linguistics-inspired model trained with a few task-specific strategies. The main techniques of our system are: 1) data augmentation, 2) multi-label loss, 3) adapted R-Drop, 4) samples reconstruction with the head-tail combination. We also present a brief analysis of some negative methods like two-tower architecture. Our system ranked 1st on the leaderboard while achieving a Pearson's Correlation Coefficient of 0.818 on the official evaluation set.
Code (1)
Tasks
Data AugmentationSimilar Papers 제목 키워드 기반
D-Bees: A Novel Method Inspired by Bee Colony Optimization for Solving Word Sense Disambiguation
Word sense disambiguation (WSD) is a problem in the field of computational linguistics given as finding the intended sense of a word (or a set of words) when it is activated within a certain context. WSD was recently add…
Combinatorial OptimizationWord Sense DisambiguationTreeMatch: A Fully Unsupervised WSD System Using Dependency Knowledge on a Specific Domain
Word sense disambiguation (WSD) is one of the main challenges in Computational Linguistics. TreeMatch is a WSD system originally developed using data from SemEval 2007 Task 7 (Coarse-grained English All-words Task) that …
Word Sense DisambiguationPetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text?
In this paper, we present our submission to the SemEval-2024 Task 8 "Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection", focusing on the detection of machine-generated texts (MGTs) …
DiversityText DetectionReproducing Neural Ensemble Classifier for Semantic Relation Extraction inScientific Papers
Within the natural language processing (NLP) community, shared tasks play an important role. They define a common goal and allowthe the comparison of different methods on the same data. SemEval-2018 Task 7 involves the i…
RelationRelation ExtractionCECL at SemEval-2019 Task 3: Using Surface Learning for Detecting Emotion in Textual Conversations
This paper describes the system developed by the Centre for English Corpus Linguistics for the SemEval-2019 Task 3: EmoContext. It aimed at classifying the emotion of a user utterance in a textual conversation as happy, …