Solving Sequential Text Classification as Board-Game Playing


  • Chen Qian Tsinghua University
  • Fuli Feng National University of Singapore
  • Lijie Wen Tsinghua University
  • Zhenpeng Chen Peking University
  • Li Lin Tsinghua University
  • Yanan Zheng Tsinghua University
  • Tat-Seng Chua National University of Singapore



Sequential Text Classification (STC) aims to classify a sequence of text fragments (e.g., words in a sentence or sentences in a document) into a sequence of labels. In addition to the intra-fragment text contents, considering the inter-fragment context dependencies is also important for STC. Previous sequence labeling approaches largely generate a sequence of labels in left-to-right reading order. However, the need for context information in making decisions varies across different fragments and is not strictly organized in a left-to-right order. Therefore, it is appealing to label the fragments that need less consideration of context information first before labeling the fragments that need more. In this paper, we propose a novel model that labels a sequence of fragments in jumping order. Specifically, we devise a dedicated board-game to develop a correspondence between solving STC and board-game playing. By defining proper game rules and devising a game state evaluator in which context clues are injected, at each round, each player is effectively pushed to find the optimal move without position restrictions via considering the current game state, which corresponds to producing a label for an unlabeled fragment jumpily with the consideration of the contexts clues. The final game-end state is viewed as the optimal label sequence. Extensive results on three representative datasets show that the proposed approach outperforms the state-of-the-art methods with statistical significance.




How to Cite

Qian, C., Feng, F., Wen, L., Chen, Z., Lin, L., Zheng, Y., & Chua, T.-S. (2020). Solving Sequential Text Classification as Board-Game Playing. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8640-8648.



AAAI Technical Track: Natural Language Processing