Shuaiqiang Wang

Pre-trained Language Model for Web-scale Retrieval in Baidu Search

2021

Retrieval is a crucial stage in web search that identifies a small set of query-relevant candidates from a billion-scale corpus. Discovering more semantically-related candidates in the retrieval stage is very promising to expose more high-quality …

Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking

2022 · arXiv (Cornell University)

Passage re-ranking is to obtain a permutation over the candidate passage set from retrieval stage. Re-rankers have been boomed by Pre-trained Language Models (PLMs) due to their overwhelming advantages in natural language understanding. However, existing …

Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes

2023 · arXiv (Cornell University)

Heterogeneous Information Networks (HINs), which consist of various types of nodes and edges, have recently demonstrated excellent performance in graph mining. However, most existing heterogeneous graph neural networks (HGNNs) ignore the problems of missing attributes, …

DiQAD: A Benchmark Dataset for Open-domain Dialogue Quality Assessment

2023

Dialogue assessment plays a critical role in the development of open-domain dialogue systems. Existing work are uncapable of providing an end-to-end and human-epistemic assessment dataset, while they only provide sub-metrics like coherence or the dialogues …

Learning to Recommend Accurate and Diverse Items

2017

In this study, we investigate diversified recommendation problem by supervised learning, seeking significant improvement in diversity while maintaining accuracy. In particular, we regard each user as a training instance, and heuristically choose a subset of …