Arrow Research search

Author name cluster

Wanting Ji

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
1 author row

Possible papers

2

EAAI Journal 2024 Journal Article

Local instance-based transfer learning for reinforcement learning

  • Xiaoguang Li
  • Wanting Ji
  • Jidong Huang

Similarity-based transfer learning for reinforcement learning has garnered attention for its potential to enhance target task learning. However, it faces significant challenges in efficiency and effectiveness, primarily stemming from issues such as sparse reward, long trajectory, and strict similarity. To solve these problems, this paper proposes a local instance-based transfer learning method for reinforcement learning. Instead of relying on sparse reward and long trajectory, this approach leverages the Q value of the local trajectory to evaluate similarity, thereby significantly enhancing transfer efficiency. Furthermore, by relaxing the strictness of the similarity, three transfer policies are proposed to facilitate positive transfer. Extensive experimental results demonstrate that the effectiveness and efficiency of the proposed method in comparison with traditional similarity-based transfer learning methods.

Details DOI

EAAI Journal 2023 Journal Article

Fine-grained document-level financial event argument extraction approach

  • Ze Chen
  • Wanting Ji
  • Linlin Ding
  • Baoyan Song

Document-level financial event argument extraction aims to extract a set of structured financial information related to particular financial events from a financial document. This task is challenging because there is complex fine-grained semantic information between financial event arguments, such as cross-document, inner-sentence and context semantic information. Existing approaches are still unable to capture these fine-grained financial semantic features well, leading to the low performance defects. Different from these existing approaches, we propose an end-to-end fine-grained document-level financial event argument extraction approach (FGD-FEAE), which defines event argument extraction as a sequence tagging task. In the encoder, a novel multiple granularity attention layer and a long-short term memory network (LSTM) extension layer are proposed and designed to effectively capture these fine-grained financial semantic information. In the decoder, to avoid the financial semantic confusion problem, a conditional random field (CRF) layer is constructed to jointly tag the financial event arguments. Finally, FGD-FEAE is tested on the two benchmark financial datasets, Chinese financial annotation dataset (ChiFinAnn) and English financial press releases (EFPR), and our own Chinese financial project dataset (ChiFD). These datasets contain a large amount of financial corpus information and complete financial event arguments and provide a solid foundation for verifying the general applicability of the proposed FGD-FEAE. Experimental results show that FGD-FEAE can achieve the better performance than the state-of-the-art approaches, and the performance is improved by 4. 69, 5. 01 and 3. 78 on the three actual financial datasets, respectively.

Details DOI
v2026.09.13