… To fine-tune LayoutLMv2 models on these tasks, we build a tokenlevel classification layer
above the text part of the output representations to predict the BIO tags for each entity field. …

… : document entity recognition, document classification, and … to the advancement of document
analysis and intelligence by … information extraction from visually rich documents. In ACL, …

… The document layout analysis task is to extract the pre-defined semantic units in visually
rich documents. … We classify all the token by the type of semantic structures on a page of the …

… A paper’s title and abstract provide rich semantic content about … For our evaluation, we
derive a document classification dataset … In this section, we analyze several design decisions in …

… imperative, given that it is rich in information and can be used … the representation of text for
low-quality text Classification and … pre-processing techniques analyzed in our study are briefly …

… We provide a quantitative analysis of the performance of a selected set of DL models on 16
… -structured network typologies, to learn rich semantic representations. The authors argue that …

… Based on the experimental results and analysis, we further … Even BiLSTM can capture more
comprehensive and rich … equally to the representation of the document. For example, in the …

… -rich regime, with lots of training examples, our method can outperform a pure classification-…
by “connecting” fine-tuned latent representations of relevance to related output “target words”…

… Text classification is one of the primary tasks in the NLP field, as it provides fundamental
methodologies for other NLP tasks, such as spam filtering, sentiment analysis, intent detection, …

… document visual question answering, but also in image-centric tasks such as document image
classification and document layout analysis. … ) aims to learn rich visual representations via …