Miracle-dz/UniFAR-FTUs huggingface dataset:
SOM-NCSCM : An Efficient Neural Chinese Sentence Compression Model Enhanced with Self-Organizing Map

Sentence Compression (SC), which aims to shorten sentences while retaining important words that express the essential meanings, has been studied for many years in many languages, especially in English. However, improvements on Chinese SC task are still quite few due to several difficulties: scarce of parallel corpora, different segmentation granularity of Chinese sentences, and imperfect performance of syntactic analyses. Furthermore, entire neural Chinese SC models have been under-investigated so far. In this work, we construct an SC dataset of Chinese colloquial sentences from a real-life question answering system in the telecommunication domain, and then, we propose a neural Chinese SC model enhanced with a Self-Organizing Map (SOM-NCSCM), to gain a valuable insight from the data and improve the performance of the whole neural Chinese SC model in a valid manner. Experimental results show that our SOM-NCSCM can significantly benefit from the deep investigation of similarity among data, and achieve a promising F1 score of 89.655 and BLEU4 score of 70.116, which also provides a baseline for further research on Chinese SC task.
STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media

Rumor detection on social media puts pre-trained language models (LMs), such as BERT, and auxiliary features, such as comments, into use. However, on the one hand, rumor detection datasets in Chinese companies with comments are rare; on the other hand, intensive interaction of attention on Transformer-based models like BERT may hinder performance improvement. To alleviate these problems, we build a new Chinese microblog dataset named Weibo20 by collecting posts and associated comments from Sina Weibo and propose a new ensemble named STANKER (Stacking neTwork bAsed-on atteNtion-masKed BERT). STANKER adopts two level-grained attention-masked BERT (LGAM-BERT) models as base encoders. Unlike the original BERT, our new LGAM-BERT model takes comments as important auxiliary features and masks co-attention between posts and comments on lower-layers. Experiments on Weibo20 and three existing social media datasets showed that STANKER outperformed all compared models, especially beating the old state-of-the-art on Weibo dataset.
Mental Workload Detection Based on EEG Analysis

The study of mental workload becomes essential for human work efficiency, health conditions and to avoid accidents, since workload compromises both performance and awareness. Although workload has been widely studied using several physiological measures, minimising the sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown a high correlation to specific cognitive and mental states like workload. However, there is not enough evidence in the literature to validate how well models generalize in case of new subjects performing tasks of a workload similar to the ones included during model’s training. In this paper we propose a binary neural network to classify EEG features across different mental workloads. Two workloads, low and medium, are induced using two variants of the N-Back Test. The proposed model was validated in a dataset collected from 16 subjects and shown a high level of generalization capability: model reported an average recall of 81.81% in a leave-one-out subject evaluation.
Event Coreference Data (Almost) for Free: Mining Hyperlinks from Online News

Cross-document event coreference resolution (CDCR) is the task of identifying which event mentions refer to the same events throughout a collection of documents. Annotating CDCR data is an arduous and expensive process, explaining why existing corpora are small and lack domain coverage. To overcome this bottleneck, we automatically extract event coreference data from hyperlinks in online news: When referring to a significant real-world event, writers often add a hyperlink to another article covering this event. We demonstrate that collecting hyperlinks which point to the same article(s) produces extensive and high-quality CDCR data and create a corpus of 2M documents and 2.7M silver-standard event mentions called HyperCoref. We evaluate a state-of-the-art system on three CDCR corpora and find that models trained on small subsets of HyperCoref are highly competitive, with performance similar to models trained on gold-standard data. With our work, we free CDCR research from depending on costly human-annotated training data and open up possibilities for research beyond English CDCR, as our data extraction approach can be easily adapted to other languages.
Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification

Data augmentation and adversarial perturbation approaches have recently achieved promising results in solving the over-fitting problem in many natural language processing (NLP) tasks including sentiment classification. However, existing studies aimed to improve the generalization ability by augmenting the training data with synonymous examples or adding random noises to word embeddings, which cannot address the spurious association problem. In this work, we propose an end-to-end reinforcement learning framework, which jointly performs counterfactual data generation and dual sentiment classification. Our approach has three characteristics:1) the generator automatically generates massive and diverse antonymous sentences; 2) the discriminator contains a original-side sentiment predictor and an antonymous-side sentiment predictor, which jointly evaluate the quality of the generated sample and help the generator iteratively generate higher-quality antonymous samples; 3) the discriminator is directly used as the final sentiment classifier without the need to build an extra one. Extensive experiments show that our approach outperforms strong data augmentation baselines on several benchmark sentiment classification datasets. Further analysis confirms our approach’s advantages in generating more diverse training samples and solving the spurious association problem in sentiment classification.
A LPV/H∞ Approach for Fuel Consumption Minimization of the PHEV with Battery Life Prolongation

Abstract This paper is concerned with a gain-scheduled approach for the energy management strategy (EMS) development of a parallel hybrid electric vehicle (PHEV). The aim is to determine the power split ratio between engine and battery that minimizes the vehicle fuel consumption. First, the system is modelled under linear fractional transformation (LFT) form with varying parameters (velocity and battery capacity) represented as an uncertainty blocks. Then a discrete-time linear parameter varying (LPV) controller which is scheduled by the values of varying parameters is synthesized using the approach proposed in Apkarian and Gahinet (1995). Finally, the performances of the LPV controller are analysed based on simulations in the frequency and time domains.
Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification

Event factuality indicates the degree of certainty about whether an event occurs in the real world. Existing studies mainly focus on identifying event factuality at sentence level, which easily leads to conflicts between different mentions of the same event. To this end, we study the problem of document-level event factuality identification, which determines the event factuality from the view of a document. For this task, we need to consider two important characteristics: Local Uncertainty and Global Structure, which can be utilized to improve performance. In this paper, we propose an Uncertain Local-to-Global Network (ULGN) to make use of these two characteristics. Specifically, we devise a Local Uncertainty Estimation module to model the uncertainty of local information. Moreover, we propose an Uncertain Information Aggregation module to leverage the global structure for integrating the local information. Experimental results demonstrate the effectiveness of our proposed method, outperforming the previous state-of-the-art model by 8.4% and 11.45% of F1 score on two widely used datasets.
Human Rationales as Attribution Priors for Explainable Stance Detection

As NLP systems become better at detecting opinions and beliefs from text, it is important to ensure not only that models are accurate but also that they arrive at their predictions in ways that align with human reasoning. In this work, we present a method for imparting human-like rationalization to a stance detection model using crowdsourced annotations on a small fraction of the training data. We show that in a data-scarce setting, our approach can improve the reasoning of a state-of-the-art classifier—particularly for inputs containing challenging phenomena such as sarcasm—at no cost in predictive performance. Furthermore, we demonstrate that attention weights surpass a leading attribution method in providing faithful explanations of our model’s predictions, thus serving as a computationally cheap and reliable source of attributions for our model.
Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically

Integrating knowledge into text is a promising way to enrich text representation, especially in the medical field. However, undifferentiated knowledge not only confuses the text representation but also imports unexpected noises. In this paper, to alleviate this problem, we propose leveraging capsule routing to associate knowledge with medical literature hierarchically (called HiCapsRKL). Firstly, HiCapsRKL extracts two empirically designed text fragments from medical literature and encodes them into fragment representations respectively. Secondly, the capsule routing algorithm is applied to two fragment representations. Through the capsule computing and dynamic routing, each representation is processed into a new representation (denoted as caps-representation), and we integrate the caps-representations as information gain to associate knowledge with medical literature hierarchically. Finally, HiCapsRKL are validated on relevance prediction and medical literature retrieval test sets. The experimental results and analyses show that HiCapsRKLcan more accurately associate knowledge with medical literature than mainstream methods. In summary, HiCapsRKL can efficiently help selecting the most relevant knowledge to the medical literature, which may be an alternative attempt to improve knowledge-based text representation. Source code is released on GitHub.
Identifying bridge structural condition development trends via categorical inspection condition rating with case studies

Inspection data in a bridge management system (BMS) may provide us with comprehensive information about a structure's condition history. Identification of bridge structural condition development trends from historic inspection data for maintenance planning has drawn more and more attention, particularly from bridge agencies, who prefer ‘worst-first’ maintenance and rehabilitation strategies. In this paper, rather than correlating inspection data with fundamental structural deterioration models, a statistical inspection data-based analysis was developed. To demonstrate the inspection data-based approach in identifying bridge structural condition development trends in practical scenarios, case studies of concrete deck slabs and steel beam girders were applied. The ANalysis Of VAriance (ANOVA) technique was adopted to identify the major factors considered by a BMS to have a significant influence on the condition index. As a result, three major factors, bridge construction year, inspection year and inspector, were identified when structures were exposed to the same environment. Associated with these major factors, statistical indicators were introduced to characterize the condition development trends of structures, and to detect signs related to the potential of threat to the structural integrity. The indicators were further applied to identify a specific subgroup, e.g. those bridges that were constructed in a certain period and experienced the most deterioration or the highest deterioration rate. The approach developed can be extended to look into more causal factors on how they may alter the structural condition development trends when their information is available within inspection records. The outcomes may offer useful information for maintenance planning. Furthermore, it was found that the inspector also had quite a considerable impact on the condition index. A difference in structural condition assessment among inspectors may occur regardless of the same structural condition scenarios. This difference was quantified and applied to identify inspectors who tended to underestimate deterioration in structural condition.
Modified circular monopole antenna with improved transmission performance for UWB communications

This study theoretically analyses the transmission performance of UWB antennas and presents a design methodology for improving the transmission performance of printed monopoles. Using this methodology, a modified circular monopole are proposed and analyzed as the design example. Both measured and simulated results are provided and discussed for verifying the validity of proposed design methodology. In a typical lab environment, the deleterious multi‐path transmission effects on the transfer function of the example antenna pair were observed and discussed. This optimization methodology would not bring any size or cost increase to the printed monopoles, and the improvement of transmission performance is quite discernible, which appears to be a significant improvement over printed UWB monopoles. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 1285–1289, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23336
A Language Model-based Generative Classifier for Sentence-level Discourse Parsing

Discourse segmentation and sentence-level discourse parsing play important roles for various NLP tasks to consider textual coherence. Despite recent achievements in both tasks, there is still room for improvement due to the scarcity of labeled data. To solve the problem, we propose a language model-based generative classifier (LMGC) for using more information from labels by treating the labels as an input while enhancing label representations by embedding descriptions for each label. Moreover, since this enables LMGC to make ready the representations for labels, unseen in the pre-training step, we can effectively use a pre-trained language model in LMGC. Experimental results on the RST-DT dataset show that our LMGC achieved the state-of-the-art F1 score of 96.72 in discourse segmentation. It further achieved the state-of-the-art relation F1 scores of 84.69 with gold EDU boundaries and 81.18 with automatically segmented boundaries, respectively, in sentence-level discourse parsing.
Affective State-Based Framework for e-Learning Systems

Virtual learning and education have become crucial during the COVID-19 pandemic, which has forced a rethink by teachers and educators into designing online content and the indirect interaction with students. In an face-to-face class, some visual cues help the teacher recognize the engagement level of students, while the main weakness of the online approach is the lack of feedback that the teacher has about the learning process of the students. In this paper, we introduce a novel framework able to track the learning states, or LS, of the students while they are watching a piece of knowledge-based content. Specifically, we extract four learning states: Interested, Bored, Confused or Distracted. Finally, to demonstrate the system’s capability, we collected a reduced database to analyze the affective state of the subjects. From these preliminary results, we observe abrupt changes in the LS of the audience when there are abrupt changes in the narrative of the video, indicating that well-structured and bounded information is strongly related with the learning behaviour of the students.
Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge

In this paper, we investigate the Aspect Category Sentiment Analysis (ACSA) task from a novel perspective by exploring a Beta Distribution guided aspect-aware graph construction based on external knowledge. That is, we are no longer entangled about how to laboriously search the sentiment clues for coarse-grained aspects from the context, but how to preferably find the words highly related to the aspects in the context and determine their importance based on the public knowledge base. In this way, the contextual sentiment clues can be explicitly tracked in ACSA for the aspects in the light of these aspect-related words. To be specific, we first regard each aspect as a pivot to derive aspect-aware words that are highly related to the aspect from external affective commonsense knowledge. Then, we employ Beta Distribution to educe the aspect-aware weight, which reflects the importance to the aspect, for each aspect-aware word. Afterward, the aspect-aware words are served as the substitutes of the coarse-grained aspect to construct graphs for leveraging the aspect-related contextual sentiment dependencies in ACSA. Experiments on 6 benchmark datasets show that our approach significantly outperforms the state-of-the-art baseline methods.
Word Reordering for Zero-shot Cross-lingual Structured Prediction

Adapting word order from one language to another is a key problem in cross-lingual structured prediction. Current sentence encoders (e.g., RNN, Transformer with position embeddings) are usually word order sensitive. Even with uniform word form representations (MUSE, mBERT), word order discrepancies may hurt the adaptation of models. In this paper, we build structured prediction models with bag-of-words inputs, and introduce a new reordering module to organizing words following the source language order, which learns task-specific reordering strategies from a general-purpose order predictor model. Experiments on zero-shot cross-lingual dependency parsing, POS tagging, and morphological tagging show that our model can significantly improve target language performances, especially for languages that are distant from the source language.
Simple Conversational Data Augmentation for Semi-supervised Abstractive Dialogue Summarization

Abstractive conversation summarization has received growing attention while most current state-of-the-art summarization models heavily rely on human-annotated summaries. To reduce the dependence on labeled summaries, in this work, we present a simple yet effective set of Conversational Data Augmentation (CODA) methods for semi-supervised abstractive conversation summarization, such as random swapping/deletion to perturb the discourse relations inside conversations, dialogue-acts-guided insertion to interrupt the development of conversations, and conditional-generation-based substitution to substitute utterances with their paraphrases generated based on the conversation context. To further utilize unlabeled conversations, we combine CODA with two-stage noisy self-training where we first pre-train the summarization model on unlabeled conversations with pseudo summaries and then fine-tune it on labeled conversations. Experiments conducted on the recent conversation summarization datasets demonstrate the effectiveness of our methods over several state-of-the-art data augmentation baselines.
On Classifying whether Two Texts are on the Same Side of an Argument

To ease the difficulty of argument stance classification, the task of same side stance classification (S3C) has been proposed. In contrast to actual stance classification, which requires a substantial amount of domain knowledge to identify whether an argument is in favor or against a certain issue, it is argued that, for S3C, only argument similarity within stances needs to be learned to successfully solve the task. We evaluate several transformer-based approaches on the dataset of the recent S3C shared task, followed by an in-depth evaluation and error analysis of our model and the task’s hypothesis. We show that, although we achieve state-of-the-art results, our model fails to generalize both within as well as across topics and domains when adjusting the sampling strategy of the training and test set to a more adversarial scenario. Our evaluation shows that current state-of-the-art approaches cannot determine same side stance by considering only domain-independent linguistic similarity features, but appear to require domain knowledge and semantic inference, too.
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations

Machine Reading Comprehension (MRC), which requires a machine to answer questions given the relevant documents, is an important way to test machines’ ability to understand human language. Multiple-choice MRC is one of the most studied tasks in MRC due to the convenience of evaluation and the flexibility of answer format. Post-hoc interpretation aims to explain a trained model and reveal how the model arrives at the prediction. One of the most important interpretation forms is to attribute model decisions to input features. Based on post-hoc interpretation methods, we assess attributions of paragraphs in multiple-choice MRC and improve the model by punishing the illogical attributions. Our method can improve model performance without any external information and model structure change. Furthermore, we also analyze how and why such a self-training method works.
Numerical Investigation of Pool Boiling Under Ocean Conditions with Lattice Boltzmann Simulation. Part Ⅰ: Heaving Condition

Pool boiling is the heat-transfer mechanism of many heat exchangers inside ocean nuclear power plants working under the complex marine circumstances. Also, ocean conditions will create a new acceleration field other than gravity for the fluid, which induces some unique thermal–hydraulic characteristics. In this study, pool boiling under heaving conditions is numerically simulated using multiple relaxation time phase change lattice Boltzmann method. Firstly, the simulated results under static condition have been validated with recognized empirical equations, such as Rohsenow’s correlation at nucleate boiling, Zuber’s model, and Kandlikar’s model about critical heat flux (CHF). Then, pool boiling patterns, the boiling curve of time-averaged heat flux, transient heat flux, and heaving effects on different pool boiling regions are investigated. The results show that pool boiling curves of time-averaged heat flux between heaving conditions and static conditions with middle superheat degrees are similar. Heat transfer under heaving conditions at low superheat is somewhat enhanced, and it is weakened at high superheat, which leads to a slightly smaller critical heat flux with larger superheat compared with that under static conditions. Moreover, distinct fluctuation of the transient heat flux of pool boiling under heaving conditions is found for all boiling regimes. Furthermore, the heaving condition shows both positive and negative effects on pool boiling heat transfer at high-gravity and low-gravity regions, respectively. Besides, both the larger heaving height and shorter period time bring out more violent heaving motion and make a greater impact on pool boiling heat transfer.