Making Sentence Embeddings Robust to User-Generated Content

arXiv:2403.17220v1 Announce Type: new
Abstract: NLP models have been known to perform poorly on user-generated content (UGC), mainly because it presents a lot of lexical variations and deviates from the standard texts on which most of these models were trained. In this work, we focus on the robustness of LASER, a sentence embedding model, to UGC data. We evaluate this robustness by LASER's ability to represent non-standard sentences and their standard counterparts close to each other in the embedding space. …

abstract arxiv cs.cl embedding embeddings focus generated making nlp nlp models robust robustness standard type ugc work