Improving Variational Encoder-Decoders in Dialogue Generation

Variational encoder-decoders (VEDs) have shown promising results in dialogue generation. However, the latent variable distributions are usually approximated by a much simpler model than the powerful RNN structure used for encoding and decoding, yielding the KL-vanishing problem and inconsistent training objective. In this paper, we separate the training step into two phases: The first phase learns to autoencode discrete texts into continuous embeddings, from which the second phase learns to generalize latent representations by reconstructing the encoded embedding. In this case, latent variables are sampled by transforming Gaussian noise through multi-layer perceptrons and are trained with a separate VED model, which has the potential of realizing a much more flexible distribution. We compare our model with current popular models and the experiment demonstrates substantial improvement in both metric-based and human evaluations.Comment: Accepted by AAAI201

A Conditional Variational Framework for Dialog Generation

Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems. However, these latent variables are highly randomized, leading to uncontrollable generated responses. In this paper, we propose a framework allowing conditional response generation based on specific attributes. These attributes can be either manually assigned or automatically detected. Moreover, the dialog states for both speakers are modeled separately in order to reflect personal features. We validate this framework on two different scenarios, where the attribute refers to genericness and sentiment states respectively. The experiment result testified the potential of our model, where meaningful responses can be generated in accordance with the specified attributes.Comment: Accepted by ACL201

Social4Rec: Distilling User Preference from Social Graph for Video Recommendation in Tencent

Despite recommender systems play a key role in network content platforms, mining the user's interests is still a significant challenge. Existing works predict the user interest by utilizing user behaviors, i.e., clicks, views, etc., but current solutions are ineffective when users perform unsettled activities. The latter ones involve new users, which have few activities of any kind, and sparse users who have low-frequency behaviors. We uniformly describe both these user-types as "cold users", which are very common but often neglected in network content platforms. To address this issue, we enhance the representation of the user interest by combining his social interest, e.g., friendship, following bloggers, interest groups, etc., with the activity behaviors. Thus, in this work, we present a novel algorithm entitled SocialNet, which adopts a two-stage method to progressively extract the coarse-grained and fine-grained social interest. Our technique then concatenates SocialNet's output with the original user representation to get the final user representation that combines behavior interests and social interests. Offline experiments on Tencent video's recommender system demonstrate the superiority over the baseline behavior-based model. The online experiment also shows a significant performance improvement in clicks and view time in the real-world recommendation system. The source code is available at https://github.com/Social4Rec/SocialNet

A New Probabilistic Model for Top-k Ranking Problem

ABSTRACT This paper is concerned with top-k ranking problem, which reflects the fact that people pay more attention to the top ranked objects in real ranking application like information retrieval. A popular approach to top-k ranking problem is based on probabilistic models, such as Luce model and Mallows model. However, whether the sequential generative process described in these models is a suitable way for top-k ranking remains a question. According to the riffled independence factorization proposed in recent literature, which is a natural structural assumption on top-k ranking, we propose a new generative process of top-k ranking data. Our approach decomposes distributions over the top-k ranking into two layers: the first layer describes the relative ordering between the top k objects and the rest n − k objects, and the second layer describes the full ordering on the top k objects. On this basis, we propose a new probabilistic model for top-k ranking problem, called hierarchical ordering model. Specifically, we use three different probabilistic models to describe different generative processes of the first layer, and Luce model to describe the sequential generative process of the second layer, thus we obtain three different specific hierarchical ordering models. We also conduct extensive experiments on benchmark datasets to show that our proposed models can outperform previous models significantly

Experimental simulation of salt transport in hierarchically nested groundwater flow systems

Groundwater age and groundwater residence time contain important information about groundwater circulation and evolutionary processes, and have been widely used in the study of groundwater circulation patterns in basins.In this paper, we simulated a three-stage flow system model through a multi-stage groundwater flow system demonstrator, and simulated the groundwater age distribution and groundwater residence time distribution based, and found that the bottom of the basin, the downstream of the regional flow, and the basin retention area responded the latest.The local flow system in the shallow part has relatively low concentration values after stabilization, the intermediate flow system is also relatively low compared to the regional flow system in the deep part, and the stagnant zone has relatively large concentration values due to salt accumulation.The groundwater age distribution curves are single-peaked, and the circulation time of the regional flow system is greater than that of the intermediate flow system than that of the local flow system.The residence time distribution monitored in the discharge zones shows that different levels of recharge will produce early, middle and late peaks, and the peaks correspond exactly to the level of the groundwater flow system.It can be judged from the peaks in the discharge zones that the groundwater is recharged from the local, intermediate or regional flow system, and thus the source of contaminants can be determined.The present research results have some significance for the evolution of groundwater circulation and the improvement of groundwater flow system theory

Differentiation between glioblastoma multiforme and metastasis from the lungs and other sites using combined clinical/routine MRI radiomics

202202 bchyVersion of RecordOthersThis work was supported by the National Natural Science Foundation of China under grant number 81772005, the National Key Research and Development Program of China Grant under grant number 2018YFC0115604, and Collaborative innovative major special project supported by Beijing Municipal Science & Technology Commission under grant number Z191100006619088.Publishe