CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data
Rui Feng, Chen Luo, Qingyu Yin, Bing Yin, Tuo Zhao, Chao Zhang
Abstract
User sessions empower many search and recommendation tasks on a daily basis. Such session data are semi-structured, which encode heterogeneous relations between queries and products, and each item is described by the unstructured text. Despite recent advances in self-supervised learning for text or graphs, there lack of self-supervised learning models that can effectively capture both intra-item semantics and inter-item interactions for semi-structured sessions. To fill this gap, we propose CERES, a graph-based transformer model for semi-structured session data. CERES learns representations that capture both inter- and intra-item semantics with (1) a graph-conditioned masked language pretraining task that jointly learns from item text and item-item relations; and (2) a graph-conditioned transformer architecture that propagates inter-item contexts to item-level representations. We pretrained CERES using ~468 million Amazon sessions and find that CERES outperforms strong pretraining baselines by up to 9% in three session search and entity linking tasks.- Anthology ID:
- 2022.naacl-main.16
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 219–230
- Language:
- URL:
- https://aclanthology.org/2022.naacl-main.16
- DOI:
- 10.18653/v1/2022.naacl-main.16
- Cite (ACL):
- Rui Feng, Chen Luo, Qingyu Yin, Bing Yin, Tuo Zhao, and Chao Zhang. 2022. CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 219–230, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data (Feng et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/2022.naacl-main.16.pdf