6 accepted papers
Sequence labeling models often benefit from incorporating external knowledge. However, this practice introduces data heterogeneity and complicates the model with additional modules, leading to increased expenses for training a high-performing model. To address this challenge, we propose a dual-stage…
With thousand years of history, calligraphy serve as one of the representative symbols of Chinese culture. Increasing works try to digitize calligraphy by recognizing the context of calligraphy for better preservation and propagation. However, previous works stick to isolated single character recogn
As the training of large language models (LLMs) will encounter high computational costs, massive works are now focusing on inference. Their methods can be generally summarised as re-sampling the target multiple times and performing a vote upon the outputs. Despite bringing significant performance im…
The research on classical Chinese event extraction trends to directly graft the complex modeling from English or modern Chinese works, neglecting the utilization of the unique characteristic of this language. We argue that, compared with grafting the sophisticated methods from other languages, focus…
Recent research work on textual Aspect-Based Sentiment Analysis (ABSA) have achieved promising performance. However, a persistent challenge lies in the limited semantics derived from the raw data. To address this issue, researchers have explored enhancing textual ABSA with additional augmentations,…
As a complex task that requires rich information input, features from various aspects have been utilized in event extraction. However, most of the previous works ignored the value of glyph, which could contain enriched semantic information and can not be fully expressed by the pre-trained embedding…