Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network

Neural conversational models learn to generate responses by taking into account the dialog history. These models are typically optimized over the query-response pairs with a maximum likelihood estimation objective. However, the query-response tuples are naturally loosely coupled, and there exist multiple responses that can respond to a given query, which leads the conversational model learning burdensome. Besides, the general dull response problem is even worsened when the model is confronted with meaningless response training instances. Intuitively, a high-quality response not only responds to the given query but also links up to the future conversations, in this paper, we leverage the query-response-future turn triples to induce the generated responses that consider both the given context and the future conversations. To facilitate the modeling of these triples, we further propose a novel encoder-decoder based generative adversarial learning framework, Posterior Generative Adversarial Network (Posterior-GAN), which consists of a forward and a backward generative discriminator to cooperatively encourage the generated response to be informative and coherent by two complementary assessment perspectives. Experimental results demonstrate that our method effectively boosts the informativeness and coherence of the generated response on both automatic and human evaluation, which verifies the advantages of considering two assessment perspectives.Comment: Accepted by AAAI 202

Starch gelatinization under shearless and shear conditions

This article reviews the development of studying starch gelatinization under shear and shearless conditions, in particular the technologies used to detect the degree of gelatinization. Advantages and disadvantages of each technology were discussed and then some examples were presented to demonstrate their application. A new technology RheoScope, an instrument that can measure viscosity under shear stress and simultaneously observes variation of starch particles using a microscope, was also introduced. It was found the definition of "gelatinization" could be different for different detection technologies. Under shearless condition full gelatinization of starch needs about ratio of water 3/starch 1, while the gelatinization under shear condition requires less water content since shear stress enhances the processing. The number of endotherm and enthalpy of gelatinization depends on amylose/amylopectin, moisture and lipid content

ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought

Recently Large Language Models (LLMs) have been proven to have strong abilities in various domains and tasks. We study the problem of prompt designing in the text-to-SQL task and attempt to improve the LLMs' reasoning ability when generating SQL queries. Besides the trivial few-shot in-context learning setting, we design our chain-of-thought (CoT) prompt with a similar method to schema linking. We provide a method named ACT-SQL to automatically generate auto-CoT exemplars and thus the whole process doesn't need manual labeling. Our approach is cost-saving since we only use the LLMs' API call once when generating one SQL query. Furthermore, we extend our in-context learning method to the multi-turn text-to-SQL task. The experiment results show that the LLMs' performance can benefit from our ACT-SQL approach. Our approach achieves SOTA performance on the Spider dev set among existing in-context learning approaches

Collaborative Group Learning

Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima. However, previous approaches typically struggle with drastically aggravated student homogenization when the number of students rises. In this paper, we propose Collaborative Group Learning, an efficient framework that aims to diversify the feature representation and conduct an effective regularization. Intuitively, similar to the human group study mechanism, we induce students to learn and exchange different parts of course knowledge as collaborative groups. First, each student is established by randomly routing on a modular neural network, which facilitates flexible knowledge communication between students due to random levels of representation sharing and branching. Second, to resist the student homogenization, students first compose diverse feature sets by exploiting the inductive bias from sub-sets of training data, and then aggregate and distill different complementary knowledge by imitating a random sub-group of students at each time step. Overall, the above mechanisms are beneficial for maximizing the student population to further improve the model generalization without sacrificing computational efficiency. Empirical evaluations on both image and text tasks indicate that our method significantly outperforms various state-of-the-art collaborative approaches whilst enhancing computational efficiency.Comment: Accepted by AAAI 2021; Camera ready versio

Thermal behaviour of high amylose cornstarch studied by DSC

The thermal behaviour of high amylose cornstarches (80% amylose content) was studied by DSC using high pressure stainless steel pans in the temperature range between 0-350 degrees C. The number of endotherms and the enthalpy of gelatinization were found to depend on moisture content. Up to four endotherms and one exotherm were determined when the moisture content was above 40%. The meaning of each endotherm has been discussed. The enthalpy of gelatinization was calculated based on the summation of all the gelatinization endotherms and found to increase with increasing water content