C3SL at SemEval-2021 Task 1: Predicting Lexical Complexity of Wordsin Specific Contexts with Sentence Embeddings


We present our approach to predicting lexical complexity of words in specific contexts, as entered LCP Shared Task 1 at SemEval 2021. The approach consists of separating sentences into smaller chunks, embedding them with Sent2Vec, and reducing the embeddings into a simpler vector used as input to a neural network, the latter for predicting the complexity of words and expressions. Results show that the pre-trained sentence embeddings are not able to capture lexical complexity from the language when applied in cross-domain applications.






	title     = {{C}3{SL} at {S}em{E}val-2021 Task 1: Predicting Lexical Complexity of Words in Specific Contexts with Sentence Embeddings},
	author    = {Almeida, Raul  and Tissot, Hegler  and Fabro, Marcos Didonet Del},
	booktitle = {Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)},
	month     = {Aug},
	year      = {2021},
	address   = {Online},
	publisher = {Association for Computational Linguistics},
	pages     = {683--687},