TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search

Understand

Text encoders based on C-DSSM or transformers have demonstrated strong performance in many Natural Language Processing (NLP) tasks.

• Low latency variants of these models have also been developed in recent years in order to apply them in the field of sponsored search which has strict computational constraints.
• However these models are not the panacea to solve all the Natural Language Understanding (NLU) challenges as the pure semantic information in the data is not sufficient to fully identify the user intents.
• We propose the TextGNN model that naturally extends the strong twin tower structured encoders with the complementary graph information from user historical behaviors, which serves as a natural guide to help us better understand the intents and hence generate better language representations.