Label-Aware Automatic Verbalizer for Few-Shot Text Classification

Prompt-based learning has shown its effectiveness in few-shot text classification. One important factor in its success is a verbalizer, which translates output from a language model into a predicted class. Notably, the simplest and widely acknowledged verbalizer employs manual labels to represent the classes. However, manual selection does not guarantee the optimality of the selected words when conditioned on the chosen language model. Therefore, we propose Label-Aware Automatic Verbalizer (LAAV), effectively augmenting the manual labels to achieve better few-shot classification results. Specifically, we use the manual labels along with the conjunction "and" to induce the model to generate more effective words for the verbalizer. The experimental results on five datasets across five languages demonstrate that LAAV significantly outperforms existing verbalizers. Furthermore, our analysis reveals that LAAV suggests more relevant words compared to similar approaches, especially in mid-to-low resource languages

Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations

A sentence is typically treated as the minimal syntactic unit used to extract valuable information from long text. However, in written Thai, there are no explicit sentence markers. Some prior works use machine learning; however, a deep learning approach has never been employed. We propose a deep learning model for sentence segmentation that includes three main contributions. First, we integrate n-gram embedding as a local representation to capture word groups near sentence boundaries. Second, to focus on the keywords of dependent clauses, we combine the model with a distant representation obtained from self-attention modules. Finally, due to the scarcity of labeled data, for which annotation is difficult and time-consuming, we also investigate two techniques that allow us to utilize unlabeled data: Cross-View Training (CVT) as a semi-supervised learning technique, and a pre-trained language model (ELMo) to improve word representation. In the experiments, our model reduced the relative error by 7.4% and 18.5% compared with the baseline models on the Orchid and UGWC datasets, respectively. Ablation studies revealed that the main contributing factor was adopting n-gram features, which were further analyzed using the interpretation technique and indicated that the model utilizes the features in the same way that humans do

Real-time semantic segmentation of gastric intestinal metaplasia using a deep learning approach

Background/Aims Previous artificial intelligence (AI) models attempting to segment gastric intestinal metaplasia (GIM) areas have failed to be deployed in real-time endoscopy due to their slow inference speeds. Here, we propose a new GIM segmentation AI model with inference speeds faster than 25 frames per second that maintains a high level of accuracy. Methods Investigators from Chulalongkorn University obtained 802 histological-proven GIM images for AI model training. Four strategies were proposed to improve the model accuracy. First, transfer learning was employed to the public colon datasets. Second, an image preprocessing technique contrast-limited adaptive histogram equalization was employed to produce clearer GIM areas. Third, data augmentation was applied for a more robust model. Lastly, the bilateral segmentation network model was applied to segment GIM areas in real time. The results were analyzed using different validity values. Results From the internal test, our AI model achieved an inference speed of 31.53 frames per second. GIM detection showed sensitivity, specificity, positive predictive, negative predictive, accuracy, and mean intersection over union in GIM segmentation values of 93%, 80%, 82%, 92%, 87%, and 57%, respectively. Conclusions The bilateral segmentation network combined with transfer learning, contrast-limited adaptive histogram equalization, and data augmentation can provide high sensitivity and good accuracy for GIM detection and segmentation

Hierarchical Multi-Label Classification: Going Beyond Generalization Trees

Traditional approach to automated classification assumes that each object should be assigned to only one out of two or more classes. However, some real-world applications digress from this generic scenario in two important ways. First, each example can belong to several classes simultaneously (multi-label classification). Second, the classes can be hierarchically ordered in the sense that some are more specific versions of others (hierarchical classification). Seeking to address both of these issues, the presented work deals with “hierarchical multi-label classification”. In non-hierarchical multi-label classification, literature survey indicates that good performance is achieved when a Support Vector Machine (SVM) is used to induce each class separately. This said, some experiments suggest that further improvement can be achieved by explicitly dealing with the problem of imbalanced training sets. The author proposes a solution in terms of a technique referred to as R-SVM; the idea is to re-adjust the SVM-hyperplane offset accordingly. Experiments in the first part of this dissertation rely on data from domains of text-categorization. More important, however, is then the second part that focuses on hierarchical multi-label classification. Here, the author proposes a new technique, HR-SVM, which constitutes a hierarchical extension of R-SVM proceeding in a top-down fashion with a new mechanism to correct errors propagated from classifiers at higher levels of the hierarchy. The system has been subjected to experiments with data from the field of gene function prediction. The results show that the new technique compares favorably with other existing approaches along various performance criteria

Fast Induction of Multiple Decision Trees in Text Categorization from Large Scale, Imbalanced, and Multi-label Data

The paper focuses on automated categorization of text documents, each labeled with one or more classes and described by tens of thousands of features. The computational costs of induction in such domains are so high as almost to disqualify the use of decision trees; the reduction of these costs is thus an important research issue. Our own solution, FDT ("fast decision-tree induction"), uses a two-pronged strategy: (1) feature-set pre-selection, and (2) induction of several trees, each from a different data subset, with the combination of the results from multiple trees with a data-fusion technique tailored to domains with imbalanced classes

Enhance Text-to-Text Transfer Transformer with Generated Questions for Thai Question Answering

Question Answering (QA) is a natural language processing task that enables the machine to understand a given context and answer a given question. There are several QA research trials containing high resources of the English language. However, Thai is one of the languages that have low availability of labeled corpora in QA studies. According to previous studies, while the English QA models could achieve more than 90% of F1 scores, Thai QA models could obtain only 70% in our baseline. In this study, we aim to improve the performance of Thai QA models by generating more question-answer pairs with Multilingual Text-to-Text Transfer Transformer (mT5) along with data preprocessing methods for Thai. With this method, the question-answer pairs can synthesize more than 100 thousand pairs from provided Thai Wikipedia articles. Utilizing our synthesized data, many fine-tuning strategies were investigated to achieve the highest model performance. Furthermore, we have presented that the syllable-level F1 is a more suitable evaluation measure than Exact Match (EM) and the word-level F1 for Thai QA corpora. The experiment was conducted on two Thai QA corpora: Thai Wiki QA and iApp Wiki QA. The results show that our augmented model is the winner on both datasets compared to other modern transformer models: Roberta and mT5

Model-Based Approach on Multi-Agent Deep Reinforcement Learning With Multiple Clusters for Peer-To-Peer Energy Trading

Peer-to-peer (P2P) energy trading system has the ability to completely revolutionize the current household energy system by sharing energy among residents. As the number of customers employing distributed energy resources (DERs) such as solar rooftops increase, innovation in the double auction market (DA) system is becoming more significant. In this paper, a novel model-based, multi-agent asynchronous advantage actor-centralized-critic with communication (MB-A3C3) approach is carried out. Previous studies are limited since they suffer from unpredictable behavior in renewable energy resources and a large number of prosumers in the peer-to-peer market. As for the model-based strategy, we forecast the trading price and trading quantity in the daily energy trading system in order to overcome unpredictable issues. For the large number of prosumers, the multi-agent and multithreading RL has been chosen as our backbone since the prosumers’ behavior can be diverse; time-series clustering is introduced based on their daily trading behavior. With its environmental model and multi-threaded mechanism, MB-A3C3 is seen to be most efficient in carrying out tasks regards time and precision. The model is conducted on a large scale real-world hourly 2012–2013 dataset of 300 households in Sydney having rooftop solar systems installed in New South Wales (NSW), Australia. Results reveal that the MB-A3C3 approach outperforms other reinforcement learning methods (MADDPG and A3C3), producing lower community energy bills for 300 households. When internal trade (trading among houses) increased and external trade (trading to the grid) decreased, our multiple agent RL (MB-A3C3) significantly lowered energy bills by 17%. In closing the gap between the real-world and theoretical problems, the algorithms herein aid in reducing customers’ electricity bills

A conflict-based confidence measure for associative classification

Associative classification has aroused significant attention recently and achieved promising results. In the rule ranking process, the confidence measure is usually used to sort the class association rules (CARs). However, it may be not good enough for a classification task due to a low discrimination power to instances in the other classes. In this paper, we propose a novel conflict-based confidence measure with an interleaving ranking strategy for re-ranking CARs in an associative classification framework, which better captures the conflict between a rule and a training data instance. In the experiments, the traditional confidence measure and our proposed conflict-based confidence measure with the interleaving ranking strategy are applied as the primary sorting criterion for CARs. The experimental results show that the proposed associative classification framework achieves promising classification accuracy with the use of the conflict-based confidence measure, particularly for an imbalanced data set