NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation

Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in building end-to-end trainable dialogue systems. Though highly efficient in learning the backbone of human-computer communications, they suffer from the problem of strongly favoring short generic responses. In this paper, we argue that a good response should smoothly connect both the preceding dialogue history and the following conversations. We strengthen this connection through mutual information maximization. To sidestep the non-differentiability of discrete natural language tokens, we introduce an auxiliary continuous code space and map such code space to a learnable prior distribution for generation purpose. Experiments on two dialogue datasets validate the effectiveness of our model, where the generated responses are closely related to the dialogue context and lead to more interactive conversations.Comment: Accepted by EMNLP201

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

Solvent Isotope Effect on Transfer Hydrogenation of H2O with Glycerine under Alkaline Hydrothermal Conditions

Solvent isotope effect was investigated with 1H-, 2H-NMR, LC-MS and Gas-MS analyses on transfer hydrogenation of H2O with glycerine under alkaline hydrothermal conditions. The results from solvent isotope studies showed that (1) the H on the β-C of lactate was almost exchanged by D2O, which suggests that the hydroxyl (-OH) group on the 2-C of glycerine was first transformed into a carbonyl (C=O) group and then was converted back into a -OH group to form lactate; (2) The presence of large amounts of D was found in the produced hydrogen gas, which shows that the water molecules acted as a reactant; and (3) D% in the produced hydrogen gas was far more than 50%, which straightforwardly shows that acetol was formed in the first place as the most probable intermediate by undergoing a dehydration reaction rather than a dehydrogenation reaction