No abstract

Knowledge selection plays an important role in knowledge-grounded dialogue, which is a challenging task to generate more informative responses by leveraging external knowledge. Recently, latent variable models have been proposed to deal with the diversity of knowledge selection by using both prior and posterior distributions over knowledge and achieve promising performance. However, these models suffer from a huge gap between prior and posterior knowledge selection. Firstly, the prior selection module may not learn to select knowledge properly because of lacking the necessary posterior information. Secondly, latent variable models suffer from the exposure bias that dialogue generation is based on the knowledge selected from the posterior distribution at training but from the prior distribution at inference. Here, we deal with these issues on two aspects: (1) We enhance the prior selection module with the necessary posterior information obtained from the specially designed Posterior Information Prediction Module (PIPM); (2) We propose a Knowledge Distillation Based Training Strategy (KDBTS) to train the decoder with the knowledge selected from the prior distribution, removing the exposure bias of knowledge selection. Experimental results on two knowledge-grounded dialogue datasets show that both PIPM and KDBTS achieve performance improvement over the state-of-theart latent variable model and their combination shows further improvement.

Aims We aimed to assess the association between gut bacterial biomarkers during early pregnancy and subsequent risk of gestational diabetes mellitus (GDM) in Chinese pregnant women. Methods Within the Tongji-Shuangliu Birth Cohort study, we conducted a nested case-control study among 201 incident GDM cases and 201 matched controls. Fecal samples were collected during early pregnancy (at 6-15 weeks), and GDM was diagnosed at 24-28 weeks of pregnancy. Community DNA isolated from fecal samples and V3-V4 region of 16S rRNA gene amplicon libraries were sequenced. Results In GDM cases versus controls, Rothia, Actinomyces, Bifidobacterium, Adlercreutzia, and Coriobacteriaceae, and Lachnospiraceae spp. were significantly reduced, while Enterobacteriaceae, Ruminococcaceae spp. and Veillonellaceae were over-represented. In addition, the abundance of Staphylococcus relative to Clostridium, Roseburia and Coriobacteriaceae as reference microorganisms were positively correlated with fasting blood glucose, 1-h and 2-h postprandial glucose levels. Adding microbial taxa to the base GDM prediction model with conventional risk factors increased the C-statistic significantly (P<0.001) from 0.69 to 0.75. Conclusions Gut microbiota during early pregnancy was associated with subsequent risk of GDM. Several beneficial and commensal gut microorganisms showed inverse relations with incident GDM, while opportunistic pathogenic members were related to higher risk of incident GDM and positively correlated with glucose levels on OGTT.

Recently, to incorporate external Knowledge Base (KB) information, one form of world knowledge, several end-to-end task-oriented dialog systems have been proposed. These models, however, tend to confound the dialog history with KB tuples and simply store them into one memory. Inspired by the psychological studies on working memory, we propose a working memory model (WMM2Seq) for dialog response generation. Our WMM2Seq adopts a working memory to interact with two separated long-term memories, which are the episodic memory for memorizing dialog history and the semantic memory for storing KB tuples. The working memory consists of a central executive to attend to the aforementioned memories, and a short-term storage system to store the "activated" contents from the longterm memories. Furthermore, we introduce a context-sensitive perceptual process for the token representations of the dialog history, and then feed them into the episodic memory. Extensive experiments on two task-oriented dialog datasets demonstrate that our WMM2Seq significantly outperforms the state-of-the-art results in several evaluation metrics.