Meta-path Augmented Response Generation

We propose a chatbot, namely Mocha to make good use of relevant entities when generating responses. Augmented with meta-path information, Mocha is able to mention proper entities following the conversation flow.Comment: AAAI 201

Component-Enhanced Chinese Character Embeddings

Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on English. In this work, we innovatively develop two component-enhanced Chinese character embedding models and their bigram extensions. Distinguished from English word embeddings, our models explore the compositions of Chinese characters, which often serve as semantic indictors inherently. The evaluations on both word similarity and text classification demonstrate the effectiveness of our models.Comment: 6 pages, 2 figures, conference, EMNLP 201

Mode Regularized Generative Adversarial Networks

Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, which can easily make training stuck or push probability mass in the wrong direction, towards that of higher concentration than that of the data generating distribution. We introduce several ways of regularizing the objective, which can dramatically stabilize the training of GAN models. We also show that our regularizers can help the fair distribution of probability mass across the modes of the data generating distribution, during the early phases of training and thus providing a unified solution to the missing modes problem.Comment: Published as a conference paper at ICLR 201

TEACHER'S MUSIC ACTIVITIES IN THE CLASSROOM AS A PREREQUISITE FOR IMPROVEMENT OF MUSIC EDUCATION

The relevance of the research is realized through the search for the music teacher’s activities that help to improve the process of music education while fostering the learner’s aesthetic and meaningful relationship to music. The object of the research is a music teacher’s activity of teaching music in general education school. The aim of the research is to analyse the improvement opportunities of a music teacher’s activity during music lessons in seventh-eighth grades in general education schools. The methods of the research include the analysis of scientific literature and documents of education; semi-structured interviews with music teachers; a written survey; quantitative and qualitative data analysis. According to the collected data, a variety of musical activities helps to develop learners’ inborn musical abilities, provide a favourable learning environment with an opportunity to develop the learners’ musicianship and presuppose their transferable skills. In this context, teachers’ active and creative musical involvement helps the learner to experience more positive emotions. Such a personal involvement and cooperation serve as a major condition for the improvement of the teachers’ musical activity. An inappropriate choice of the activities can lead to dissatisfaction and be the main reason for failure. The teachers’ participation in musical activities could encompass a number of music modes, which could create a more attractive and productive activity in the lesson:  a teacher – a performer – a listener – a facilitator – a leader

Mechanism, Model, and Upscaling of the Gas Flow in Shale Matrix: Revisit

Shale gas accounts for an increasing proportion in the world’s oil and gas supply, with the properties of low carbon, clean production, and huge potential for the compensation for the gradually depleted conventional resources. Due to the ubiquitous nanopores in shale matrix, the nanoscale gas flow becomes one of the most vital themes that are directly related to the formulation of shale gas development schemes, including the optimization of hydraulic fracturing, horizontal well spacing, etc. With regard to the gas flow in shale matrix, no commonly accepted consensus has been reached about the flow mechanisms to be considered, the coupled flow model in nanopores, and the upscaling method for its macroscopic form. In this chapter, the propositions of wall-associated diffusion, a physically sound flow mechanism scheme, a new coupled flow model in nanopores, the upscaling form of the proposed model, and the translation of lab-scale results into field-scale ones aim to solve the aforementioned issues. It is expected that this work will contribute to a deeper understanding of the intrinsic relationship among various flow mechanisms and the extension of the flow model to full flow regimes and to upscaling shale matrix, thus establishing a unified model for better guiding shale gas development

Prompt-based Effective Input Reformulation for Legal Case Retrieval

Legal case retrieval plays an important role for legal practitioners to effectively retrieve relevant cases given a query case. Most existing neural legal case retrieval models directly encode the whole legal text of a case to generate a case representation, which is then utilised to conduct a nearest neighbour search for retrieval. Although these straightforward methods have achieved improvement over conventional statistical methods in retrieval accuracy, two significant challenges are identified in this paper: (1) Legal feature alignment: the usage of the whole case text as the input will generally incorporate redundant and noisy information because, from the legal perspective, the determining factor of relevant cases is the alignment of key legal features instead of whole text matching; (2) Legal context preservation: furthermore, since the existing text encoding models usually have an input length limit shorter than the case, the whole case text needs to be truncated or divided into paragraphs, which leads to the loss of the global context of legal information. In this paper, a novel legal case retrieval framework, PromptCase, is proposed to tackle these challenges. Firstly, legal facts and legal issues are identified and formally defined as the key features facilitating legal case retrieval based on a thorough study of the definition of relevant cases from a legal perspective. Secondly, with the determining legal features, a prompt-based encoding scheme is designed to conduct an effective encoding with language models. Extensive zero-shot experiments have been conducted on two benchmark datasets in legal case retrieval, which demonstrate the superior retrieval effectiveness of the proposed PromptCase. The code has been released on https://github.com/yanran-tang/PromptCase

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