Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus

Many efforts of research are devoted to semantic role labeling (SRL) which is crucial for natural language understanding. Supervised approaches have achieved impressing performances when large-scale corpora are available for resource-rich languages such as English. While for the low-resource languages with no annotated SRL dataset, it is still challenging to obtain competitive performances. Cross-lingual SRL is one promising way to address the problem, which has achieved great advances with the help of model transferring and annotation projection. In this paper, we propose a novel alternative based on corpus translation, constructing high-quality training datasets for the target languages from the source gold-standard SRL annotations. Experimental results on Universal Proposition Bank show that the translation-based method is highly effective, and the automatic pseudo datasets can improve the target-language SRL performances significantly.Comment: Accepted at ACL 202

Concept-Based Approach in Writing Instruction: The Effect of Concept Model

This paper reports the effect of concept model as mediation in writing instruction. Concept in this study refers to the generalizing language in an argumentative essay (e.g. thesis statement, topic sentence, wrap-up sentence and restatement of thesis) since such language constitutes the basic structure of an essay. Based on Ferreira and Lantolf (2008), a five-week experiment was performed, in which “movement from the abstract to the concrete†approach was used. The experiment procedure consisted of four steps: facing problems, producing concept models, revising concept models and applying concept models. But the control group experienced a traditional approach, “movement from the concrete to the abstractâ€. The results manifest the facilitating effect of concept model on knowledge internalization

D-STEM: a Design led approach to STEM innovation

Advances in the Science, Technology, Engineering and Maths (STEM) disciplines offer opportunities for designers to propose and make products with advanced, enhanced and engineered properties and functionalities. In turn, these advanced characteristics are becoming increasingly necessary as resources become ever more strained through 21st century demands, such as ageing populations, connected communities, depleting raw materials, waste management and energy supply. We need to make things that are smarter, make our lives easier, better and simpler. The products of tomorrow need to do more with less. The issue is how to maximize the potential for exploiting opportunities offered by STEM developments and how best to enable designers to strengthen their position within the innovation ecosystem. As a society, we need designers able to navigate emerging developments from the STEM community to a level that enables understanding and knowledge of the new material properties, the skill set to facilitate absorption into the design ‘toolbox’ and the agility to identify, manage and contextualise innovation opportunities emerging from STEM developments. This paper proposes the blueprint for a new design led approach to STEM innovation that begins to redefine studio culture for the 21st Century

Enhancing Subtask Performance of Multi-modal Large Language Model

Multi-modal Large Language Model (MLLM) refers to a model expanded from a Large Language Model (LLM) that possesses the capability to handle and infer multi-modal data. Current MLLMs typically begin by using LLMs to decompose tasks into multiple subtasks, then employing individual pre-trained models to complete specific subtasks, and ultimately utilizing LLMs to integrate the results of each subtasks to obtain the results of the task. In real-world scenarios, when dealing with large projects, it is common practice to break down the project into smaller sub-projects, with different teams providing corresponding solutions or results. The project owner then decides which solution or result to use, ensuring the best possible outcome for each subtask and, consequently, for the entire project. Inspired by this, this study considers selecting multiple pre-trained models to complete the same subtask. By combining the results from multiple pre-trained models, the optimal subtask result is obtained, enhancing the performance of the MLLM. Specifically, this study first selects multiple pre-trained models focused on the same subtask based on distinct evaluation approaches, and then invokes these models in parallel to process input data and generate corresponding subtask results. Finally, the results from multiple pre-trained models for the same subtask are compared using the LLM, and the best result is chosen as the outcome for that subtask. Extensive experiments are conducted in this study using GPT-4 annotated datasets and human-annotated datasets. The results of various evaluation metrics adequately demonstrate the effectiveness of the proposed approach in this paper

On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training

Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities toward certain aspects of products or services behind the social media texts or reviews, which has been a fundamental application to the real-world society. Since the early 2010s, ABSA has achieved extraordinarily high accuracy with various deep neural models. However, existing ABSA models with strong in-house performances may fail to generalize to some challenging cases where the contexts are variable, i.e., low robustness to real-world environments. In this study, we propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training. First, we strengthen the current best-robust syntax-aware models by further incorporating the rich external syntactic dependencies and the labels with aspect simultaneously with a universal-syntax graph convolutional network. In the corpus perspective, we propose to automatically induce high-quality synthetic training data with various types, allowing models to learn sufficient inductive bias for better robustness. Last, we based on the rich pseudo data perform adversarial training to enhance the resistance to the context perturbation and meanwhile employ contrastive learning to reinforce the representations of instances with contrastive sentiments. Extensive robustness evaluations are conducted. The results demonstrate that our enhanced syntax-aware model achieves better robustness performances than all the state-of-the-art baselines. By additionally incorporating our synthetic corpus, the robust testing results are pushed with around 10% accuracy, which are then further improved by installing the advanced training strategies. In-depth analyses are presented for revealing the factors influencing the ABSA robustness.Comment: Accepted in ACM Transactions on Information System