Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification

Text classification must sometimes be applied in a low-resource language with no labeled training data. However, training data may be available in a related language. We investigate whether character-level knowledge transfer from a related language helps text classification. We present a cross-lingual document classification framework (CACO) that exploits cross-lingual subword similarity by jointly training a character-based embedder and a word-based classifier. The embedder derives vector representations for input words from their written forms, and the classifier makes predictions based on the word vectors. We use a joint character representation for both the source language and the target language, which allows the embedder to generalize knowledge about source language words to target language words with similar forms. We propose a multi-task objective that can further improve the model if additional cross-lingual or monolingual resources are available. Experiments confirm that character-level knowledge transfer is more data-efficient than word-level transfer between related languages.Comment: AAAI 202

Work-Family Conflict and Unethical Pro-family Behavior: The Mediating Effect of Threat Appraisal and the Moderating Effect of Family Collectivism Orientation

Unethical pro-family behavior (UPFB) is prevalent in organizations and has adverse effects on organizations, but very few studies have examined the factors that lead to UPFB. We use a cognitive appraisal theoretical framework to argue that employees’ unethical pro-family (UPFB) behavior results from work and family conflicts (WFC/FWC) are mediated by threat appraisal and moderated family collectivism orientation. Based on the questionnaire data of 496 full-time employees from two-time points, we found that WFC/FWC was positively correlated with UPFB where threat appraisal played a mediating role in this relationship; Family collectivism orientation strengthens the threat appraisal-UPFB relationship and the mediation relationship between WFC/FWC and UPFB via threat appraisal. These findings offer an understanding of the theoretical and practical implications which could help organizations reduce UPFB. Finally, we discuss possible directions for future research

Evaluating Hallucinations in Chinese Large Language Models

In this paper, we establish a benchmark named HalluQA (Chinese Hallucination Question-Answering) to measure the hallucination phenomenon in Chinese large language models. HalluQA contains 450 meticulously designed adversarial questions, spanning multiple domains, and takes into account Chinese historical culture, customs, and social phenomena. During the construction of HalluQA, we consider two types of hallucinations: imitative falsehoods and factual errors, and we construct adversarial samples based on GLM-130B and ChatGPT. For evaluation, we design an automated evaluation method using GPT-4 to judge whether a model output is hallucinated. We conduct extensive experiments on 24 large language models, including ERNIE-Bot, Baichuan2, ChatGLM, Qwen, SparkDesk and etc. Out of the 24 models, 18 achieved non-hallucination rates lower than 50%. This indicates that HalluQA is highly challenging. We analyze the primary types of hallucinations in different types of models and their causes. Additionally, we discuss which types of hallucinations should be prioritized for different types of models.Comment: Work in progres

Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth

International audienceGraph Neural Networks (GNNs) have been studied through the lens of expressive power and generalization. However, their optimization properties are less well understood. We take the first step towards analyzing GNN training by studying the gradient dynamics of GNNs. First, we analyze linearized GNNs and prove that despite the non-convexity of training, convergence to a global minimum at a linear rate is guaranteed under mild assumptions that we validate on real-world graphs. Second, we study what may affect the GNNs' training speed. Our results show that the training of GNNs is implicitly accelerated by skip connections, more depth, and/or a good label distribution. Empirical results confirm that our theoretical results for linearized GNNs align with the training behavior of nonlinear GNNs. Our results provide the first theoretical support for the success of GNNs with skip connections in terms of optimization, and suggest that deep GNNs with skip connections would be promising in practice

Methanol-to-olefin induction reaction over SAPO-34

The methanol-to-olefin induction reaction over the SAPO-34 was performed using a fluidized-bed system. We found that the whole induction period could be divided into three reaction stages. Further investigation of the reaction kinetics revealed that this induction reaction behavior was different from that over H-ZSM-5 catalyst. Compared with the H-ZSM-5, the generation of initial active centers is easier over SAPO-34 because of its limited diffusivity and the spatial confinement effect of the cages. However, the autocatalysis reaction stage is difficult over SAPO-34 because of the continuous formation of inactive methyladamantanes. (C) 2016, Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved