This paper studies the problem of multilingual causal reasoning in resource-poor languages. Existing approaches, translating into the most probable resource-rich language such as English, suffer in the presence of translation and language gaps between different cultural area, which leads to the loss of causality. To overcome these challenges, our goal is thus to identify key techniques to construct a new causality network of cause-effect terms, targeted for the machine-translated English, but without any language-specific knowledge of resource-poor languages. In our evaluations with three languages, Korean, Chinese, and French, our proposed method consistently outperforms all baselines, achieving up-to 69.0% reasoning accuracy, which is close to the state-of-the-art accuracy 70.2% achieved on English.
|Title of host publication||32nd AAAI Conference on Artificial Intelligence, AAAI 2018|
|Number of pages||8|
|Publication status||Published - 2018|
|Event||32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States|
Duration: 2018 Feb 2 → 2018 Feb 7
|Name||32nd AAAI Conference on Artificial Intelligence, AAAI 2018|
|Other||32nd AAAI Conference on Artificial Intelligence, AAAI 2018|
|Period||18/2/2 → 18/2/7|
Bibliographical noteFunding Information:
This work was supported by Microsoft Research, and Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2017-0-01778,Development of Explainable Human-level Deep Machine Learning Inference Framework). S. Hwang is a corresponding author.
This work was supported by Microsoft Research, and Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT)
Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence