most citedCharacter, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.NE2023

Towards Running Time Analysis of Interactive Multi-objective Evolutionary Algorithms

Tianhao Lu, Chao Bian, Chao Qian

Evolutionary algorithms (EAs) are widely used for multi-objective optimization due to their population-based nature. Traditional multi-objective EAs (MOEAs) generate a large set of…

cs.DS2023

Submodular Maximization under the Intersection of Matroid and Knapsack Constraints

Yu-Ran Gu, Chao Bian, Chao Qian

Submodular maximization arises in many applications, and has attracted a lot of research attentions from various areas such as artificial intelligence, finance and operations resea…

cs.CL2023

Retrieval-Augmented Classification with Decoupled Representation

Xinnian Liang, Shuangzhi Wu, Hui Huang +3

Retrieval augmented methods have shown promising results in various classification tasks. However, existing methods focus on retrieving extra context to enrich the input, which is…

cs.CL20232 cited

Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models

Xinnian Liang, Zefan Zhou, Hui Huang +5

Pretrained language models (PLMs) have shown marvelous improvements across various NLP tasks. Most Chinese PLMs simply treat an input text as a sequence of characters, and complete…

cs.CL2023

Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality

Xinnian Liang, Shuangzhi Wu, Chenhao Cui +3

Dialogue summarization aims to condense a given dialogue into a simple and focused summary text. Typically, both the roles' viewpoints and conversational topics change in the dialo…