6 papers
Understanding Interpretation Difficulty in Harmful Online Communication: Insights from Cybercrime Communities
Tomohiro Okatsu, Naoki Takada, Yin Min Pa Pa +2
Harmful online communication often contains slang, coded terms, abbreviations, and community-specific expressions, which make messages difficult to interpret. This paper presents a…
DD-GEPA: Prompt Optimization for Dialogue Disentanglement Focusing on Task Instruction and Utterance Representation
Naoki Takada, Tatsunori Mori
Multi-party chat often contains interleaved dialogues because multiple participants can discuss different topics at the same time. Dialogue disentanglement addresses this problem b…
A Multilingual Dataset and Empirical Validation for the Mutual Reinforcement Effect in Information Extraction
Chengguang Gan, Sunbowen Lee, Qingyu Yin +9
The Mutual Reinforcement Effect (MRE) describes a phenomenon in information extraction where word-level and sentence-level tasks can mutually improve each other when jointly modele…
GuideWeb: A Benchmark for Automatic In-App Guide Generation on Real-World Web UIs
Chengguang Gan, Yoshihiro Tsujii, Yunhao Liang +3
Digital Adoption Platform (DAP) provide web-based overlays that deliver operation guidance and contextual hints to help users navigate complex websites. Although modern DAP tools e…
M-MRE: Extending the Mutual Reinforcement Effect to Multimodal Information Extraction
Chengguang Gan, Zhixi Cai, Yanbin Wei +3
Mutual Reinforcement Effect (MRE) is an emerging subfield at the intersection of information extraction and model interpretability. MRE aims to leverage the mutual understanding be…
Empirical Study of Mutual Reinforcement Effect and Application in Few-shot Text Classification Tasks via Prompt
Chengguang Gan, Tatsunori Mori
The Mutual Reinforcement Effect (MRE) investigates the synergistic relationship between word-level and text-level classifications in text classification tasks. It posits that the p…