IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models

This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair. We propose a game theoretical minimax game to iteratively optimise both models. On one hand, the discriminative model, aiming to mine signals from labelled and unlabelled data, provides guidance to train the generative model towards fitting the underlying relevance distribution over documents given the query. On the other hand, the generative model, acting as an attacker to the current discriminative model, generates difficult examples for the discriminative model in an adversarial way by minimising its discrimination objective. With the competition between these two models, we show that the unified framework takes advantage of both schools of thinking: (i) the generative model learns to fit the relevance distribution over documents via the signals from the discriminative model, and (ii) the discriminative model is able to exploit the unlabelled data selected by the generative model to achieve a better estimation for document ranking. Our experimental results have demonstrated significant performance gains as much as 23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of applications including web search, item recommendation, and question answering.Comment: 12 pages; appendix adde

PEPT: Expert Finding Meets Personalized Pre-training

Finding appropriate experts is essential in Community Question Answering (CQA) platforms as it enables the effective routing of questions to potential users who can provide relevant answers. The key is to personalized learning expert representations based on their historical answered questions, and accurately matching them with target questions. There have been some preliminary works exploring the usability of PLMs in expert finding, such as pre-training expert or question representations. However, these models usually learn pure text representations of experts from histories, disregarding personalized and fine-grained expert modeling. For alleviating this, we present a personalized pre-training and fine-tuning paradigm, which could effectively learn expert interest and expertise simultaneously. Specifically, in our pre-training framework, we integrate historical answered questions of one expert with one target question, and regard it as a candidate aware expert-level input unit. Then, we fuse expert IDs into the pre-training for guiding the model to model personalized expert representations, which can help capture the unique characteristics and expertise of each individual expert. Additionally, in our pre-training task, we design: 1) a question-level masked language model task to learn the relatedness between histories, enabling the modeling of question-level expert interest; 2) a vote-oriented task to capture question-level expert expertise by predicting the vote score the expert would receive. Through our pre-training framework and tasks, our approach could holistically learn expert representations including interests and expertise. Our method has been extensively evaluated on six real-world CQA datasets, and the experimental results consistently demonstrate the superiority of our approach over competitive baseline methods

Exploiting Diverse Characteristics and Adversarial Ambivalence for Domain Adaptive Segmentation

Adapting semantic segmentation models to new domains is an important but challenging problem. Recently enlightening progress has been made, but the performance of existing methods are unsatisfactory on real datasets where the new target domain comprises of heterogeneous sub-domains (e.g., diverse weather characteristics). We point out that carefully reasoning about the multiple modalities in the target domain can improve the robustness of adaptation models. To this end, we propose a condition-guided adaptation framework that is empowered by a special attentive progressive adversarial training (APAT) mechanism and a novel self-training policy. The APAT strategy progressively performs condition-specific alignment and attentive global feature matching. The new self-training scheme exploits the adversarial ambivalences of easy and hard adaptation regions and the correlations among target sub-domains effectively. We evaluate our method (DCAA) on various adaptation scenarios where the target images vary in weather conditions. The comparisons against baselines and the state-of-the-art approaches demonstrate the superiority of DCAA over the competitors.Comment: Accepted to AAAI 202

Association of the low-density lipoprotein cholesterol/high-density lipoprotein cholesterol ratio and concentrations of plasma lipids with high-density lipoprotein subclass distribution in the Chinese population

<p>Abstract</p> <p>Background</p> <p>To evaluate the relationship between the low-density lipoprotein cholesterol (LDL-C)/high-density lipoprotein cholesterol (HDL-C) ratio and HDL subclass distribution and to further examine and discuss the potential impact of LDL-C and HDL-C together with TG on HDL subclass metabolism.</p> <p>Results</p> <p>Small-sized preβ₁-HDL, HDL_3band HDL_3aincreased significantly while large-sized HDL_2aand HDL_2bdecreased significantly as the LDL-C/HDL-C ratio increased. The subjects in low HDL-C level (< 1.03 mmol/L) who had an elevation of the LDL-C/HDL-C ratio and a reduction of HDL_2b/preβ₁-HDL regardless of an undesirable or high LDL-C level. At desirable LDL-C levels (< 3.34 mmol/L), the HDL_2b/preβ₁-HDL ratio was 5.4 for the subjects with a high HDL-C concentration (≥ 1.55 mmol/L); however, at high LDL-C levels (≥ 3.36 mmol/L), the ratio of LDL-C/HDL-C was 2.8 in subjects, and an extremely low HDL_2b/preβ₁-HDL value although with high HDL-C concentration.</p> <p>Conclusion</p> <p>With increase of the LDL-C/HDL-C ratio, there was a general shift toward smaller-sized HDL particles, which implied that the maturation process of HDL was blocked. High HDL-C concentrations can regulate the HDL subclass distribution at desirable and borderline LDL-C levels but cannot counteract the influence of high LDL-C levels on HDL subclass distribution.</p