Improving Medical Dialogue Generation with Abstract Meaning Representations

Medical Dialogue Generation serves a critical role in telemedicine by facilitating the dissemination of medical expertise to patients. Existing studies focus on incorporating textual representations, which have limited their ability to represent the semantics of text, such as ignoring important medical entities. To enhance the model's understanding of the textual semantics and the medical knowledge including entities and relations, we introduce the use of Abstract Meaning Representations (AMR) to construct graphical representations that delineate the roles of language constituents and medical entities within the dialogues. In this paper, We propose a novel framework that models dialogues between patients and healthcare professionals using AMR graphs, where the neural networks incorporate textual and graphical knowledge with a dual attention mechanism. Experimental results show that our framework outperforms strong baseline models in medical dialogue generation, demonstrating the effectiveness of AMR graphs in enhancing the representations of medical knowledge and logical relationships. Furthermore, to support future research in this domain, we provide the corresponding source code at https://github.com/Bernard-Yang/MedDiaAMR.Comment: Submitted to ICASSP 202

Build A Multi-Dimensional Collaborative Logistics Talent Training Model -Taking Chengdu University of Information Technology as an example

The “Opinions of the Ministry of Education on Accelerating the Construction of High-level Undergraduate Education to Comprehensively Improve the Ability of Talent Training (Jiao Gao [2018] No. 2)” clearly requires that the formation of a high-level undergraduate talent training system should be accelerated around the overall improvement of talent training capabilities. After 18 years of development, the logistics major of our school has made certain achievements in professional construction, teacher team construction, teacher teaching ability construction and experimental teaching guarantee construction, forming a relatively complete talent training system, but the social adaptation of talent training Sex still needs to be improved. This paper adheres to the problem orientation, based on the concept of collaborative education, and builds a multi-dimensional collaborative education talent training model from multiple perspectives, such as promoting the integration of production and education, condensing professional characteristics, implementing segmented training and building a capacity improvement system, and comprehensively improving the ability of logistics personnel training

Non uniform shrinkages of double-walled carbon nanotube as induced by electron beam irradiation

Electron beam-induced nanoinstabilities of pristine double-walled carbon nanotubes (DWCNTs) of two different configurations, one fixed at both ends and another fixed at only one end, were in-situ investigated in transmission electron microscope at room temperature. It was observed that the DWCNT fixed at both ends shrank in its diameter uniformly. Meanwhile, the DWCNT fixed at only one end intriguingly shrank preferentially from its free cap end along its axial direction whereas its diameter shrinkage was offset. A mechanism of "diffusion" along with "evaporation" at room temperature which is driven by the nanocurvature of the DWCNTs, and the athermal activation induced by the electron beam was proposed to elucidate the observed phenomena. The effect of the interlayer interaction of the DWCNTs was also discussed

Effective Distillation of Table-based Reasoning Ability from LLMs

Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, their remarkable parameter size and their impressive high requirement of computing resources pose challenges for their practical deployment. Recent research has revealed that specific capabilities of LLMs, such as numerical reasoning, can be transferred to smaller models through distillation. Some studies explore the potential of leveraging LLMs to perform table-based reasoning. Nevertheless, prior to our work, there has been no investigation into the prospect of specialising table reasoning skills in smaller models specifically tailored for table-to-text generation tasks. In this paper, we propose a novel table-based reasoning distillation, with the aim of distilling distilling LLMs into tailored, smaller models specifically designed for table-based reasoning task. Experimental results have shown that a 0.22 billion parameter model (Flan-T5-base) fine-tuned using distilled data, not only achieves a significant improvement compared to traditionally fine-tuned baselines but also surpasses specific LLMs like gpt-3.5-turbo on the scientific table-to-text generation dataset (SciGen). The code and data are released in https://github.com/Bernard-Yang/TableDistill

Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information

The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i.e., there may be multiple suitable responses which differ in semantics for a given conversational context. To tackle this challenge, we propose a novel learning-based automatic evaluation metric (CMN), which can robustly evaluate open-domain dialogues by augmenting Conditional Variational Autoencoders (CVAEs) with a Next Sentence Prediction (NSP) objective and employing Mutual Information (MI) to model the semantic similarity of text in the latent space. Experimental results on two open-domain dialogue datasets demonstrate the superiority of our method compared with a wide range of baselines, especially in handling responses which are distant to the golden reference responses in semantics.Comment: Accepted at ACL202

Audio Contrastive based Fine-tuning

Audio classification plays a crucial role in speech and sound processing tasks with a wide range of applications. There still remains a challenge of striking the right balance between fitting the model to the training data (avoiding overfitting) and enabling it to generalise well to a new domain. Leveraging the transferability of contrastive learning, we introduce Audio Contrastive-based Fine-tuning (AudioConFit), an efficient approach characterised by robust generalisability. Empirical experiments on a variety of audio classification tasks demonstrate the effectiveness and robustness of our approach, which achieves state-of-the-art results in various settings.Comment: Under revie

HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models

Fairness has become a trending topic in natural language processing (NLP), which addresses biases targeting certain social groups such as genders and religions. However, regional bias in language models (LMs), a long-standing global discrimination problem, still remains unexplored. This paper bridges the gap by analysing the regional bias learned by the pre-trained language models that are broadly used in NLP tasks. In addition to verifying the existence of regional bias in LMs, we find that the biases on regional groups can be strongly influenced by the geographical clustering of the groups. We accordingly propose a HiErarchical Regional Bias evaluation method (HERB) utilising the information from the sub-region clusters to quantify the bias in pre-trained LMs. Experiments show that our hierarchical metric can effectively evaluate the regional bias with respect to comprehensive topics and measure the potential regional bias that can be propagated to downstream tasks. Our codes are available at https://github.com/Bernard-Yang/HERB.Comment: Accepted at AACL 2022 as Long Finding

A new crystal: Layer-structured rhombohedral In3Se4

A new layer-structured rhombohedral In3Se4 crystal was synthesized by a facile and mild solvothermal method. Detailed structural and chemical characterizations using transmission electron microscopy, coupled with synchrotron X-ray diffraction analysis and Rietveld refinement, indicate that In3Se4 crystallizes in a layered rhombohedral structure with lattice parameters of a = 3.964 ± 0.002 Å and c = 39.59 ± 0.02 Å, a space group of R3m, and with a layer composition of Se-In-Se-In-Se-In-Se. The theoretical modeling and experimental measurements indicate that the In3Se4 is a self-doped n-type semiconductor. This study not only enriches the understanding on crystallography of indium selenide crystals, but also paves a way in the search for new semiconducting compounds. This journal i

Ethyne Reducing Metal-Organic Frameworks to Control Fabrications of Core/shell Nanoparticles as Catalysts

An approach using cobalt metal-organic frameworks (Co-MOF) as precursors is established for the fabrication of cobalt nanoparticles in porous carbon shells (core/shell Co@C). Chemical vapor deposition of ethyne is used for controlling the reduction of cobalt nanoclusters in the MOF and the spontaneous formation of the porous carbon shells. The metallic cobalt cores formed are up to 4 - 6 nm with the crystal phase varying between hexagonally-close-packed (hcp) and face-centre-packed (fcc). The porous carbon shells change from amorphous to graphene with the ethyne deposition temperature increasing from 400 to 600 oC. The core/shell Co@C nanoparticles exhibit high catalytic activity in selectively converting syngas (CTY: 254.1 - 312.1 μmolCO·gCo-1·s-1) into hydrocarbons (4.0 - 5.2 gHC·g-cat-1·h-1) at 260 oC. As well as the crystal size and phase, the coordination numbers of the cobalt to oxygen and to other cobalt atoms on the surface of the cobalt nanoparticles, and the permeability of the porous carbon shell have been related to the catalytic performance in FTS reactions