Shiqi Shen

Unbiased Sequential Recommendation with Latent Confounders

2022 · Proceedings of the ACM Web Conference 2022

Sequential recommendation holds the promise of understanding user preference by capturing successive behavior correlations. Existing research focus on designing different models for better fitting the offline datasets. However, the observational data may have been contaminated …

Sequential Recommendation with Decomposed Item Feature Routing

2022 · Proceedings of the ACM Web Conference 2022

Sequential recommendation basically aims to capture user evolving preference. Intuitively, a user interacts with an item usually because of some specific feature, and user evolving preference is essentially determined by a series of important features …

Neural Relation Extraction with Selective Attention over Instances

2016

Distant supervised relation extraction has been widely used to find novel relational facts from text. However, distant supervision inevitably accompanies with the wrong labelling problem, and these noisy data will substantially hurt the performance of …

THUMT: An Open Source Toolkit for Neural Machine Translation

2017 · arXiv (Cornell University)

This paper introduces THUMT, an open-source toolkit for neural machine translation (NMT) developed by the Natural Language Processing Group at Tsinghua University. THUMT implements the standard attention-based encoder-decoder framework on top of Theano and supports …

Minimum Risk Training for Neural Machine Translation

2016

We propose minimum risk training for end-to-end neural machine translation. Unlike conventional maximum likelihood estimation, minimum risk training is capable of optimizing model parameters directly with respect to arbitrary evaluation metrics, which are not necessarily …