Standard natural language processing (NLP) pipelines operate on symbolic representations of language, which typically consist of sequences of discrete tokens.However, creating an analogous representation for ancient logographic writing systems is an extremely laborintensive process that requires expert knowledge.At present, a large portion of logographic data persists in a purely visual form due to the absence of transcription-this issue poses a bottleneck for researchers seeking to apply NLP toolkits to study ancient logographic languages: most of the relevant data are images of writing.This paper investigates whether direct processing of visual representations of language offers a potential solution.We introduce LogogramNLP, the first benchmark enabling NLP analysis of ancient logographic languages, featuring both transcribed and visual datasets for four writing systems along with annotations for tasks like classification, translation, and parsing.Our experiments compare systems that employ recent visual and text encoding strategies as backbones.The results demonstrate that visual representations outperform textual representations for some investigated tasks, suggesting that visual processing pipelines may unlock a large amount of cultural heritage data of logographic languages for NLP-based analyses.

Recently, neural networks have achieved great success on sentiment classification due to their ability to alleviate feature engineering.However, one of the remaining challenges is to model long texts in document-level sentiment classification under a recurrent architecture because of the deficiency of the memory unit.To address this problem, we present a Cached Long Short-Term Memory neural networks (CLSTM) to capture the overall semantic information in long texts.CLSTM introduces a cache mechanism, which divides memory into several groups with different forgetting rates and thus enables the network to keep sentiment information better within a recurrent unit.The proposed CLSTM outperforms the state-of-the-art models on three publicly available document-level sentiment analysis datasets.