In sentence classification tasks, additional contexts, such as the neighboring sentences, may improve the accuracy of the classifier. However, such contexts are domain-dependent and thus cannot be used for another classification task with an inappropriate domain. In contrast, we propose the use of translated sentences as domain-free context that is always available regardless of the domain. We find that naive feature expansion of translations gains only marginal improvements and may decrease the performance of the classifier, due to possible inaccurate translations thus producing noisy sentence vectors. To this end, we present multiple context fixing attachment (MCFA), a series of modules attached to multiple sentence vectors to fix the noise in the vectors using the other sentence vectors as context. We show that our method performs competitively compared to previous models, achieving best classification performance on multiple data sets. We are the first to use translations as domainfree contexts for sentence classification.
|Title of host publication||Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018|
|Publisher||International Joint Conferences on Artificial Intelligence|
|Number of pages||7|
|Publication status||Published - 2018|
|Event||27th International Joint Conference on Artificial Intelligence, IJCAI 2018 - Stockholm, Sweden|
Duration: 2018 Jul 13 → 2018 Jul 19
|Name||IJCAI International Joint Conference on Artificial Intelligence|
|Other||27th International Joint Conference on Artificial Intelligence, IJCAI 2018|
|Period||18/7/13 → 18/7/19|
Bibliographical noteFunding Information:
This work was supported by Microsoft Research Asia and the ICT R&D program of MSIT/IITP. [2017-0-01778, Development of Explainable Human-level Deep Machine Learning Inference Framework]
© 2018 International Joint Conferences on Artificial Intelligence. All right reserved.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence