437 research outputs found
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
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