Reviewerly/exHarmony huggingface dataset:
Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model

Aspect-based sentiment analysis (ABSA) task consists of three typical subtasks: aspect term extraction, opinion term extraction, and sentiment polarity classification.These three subtasks are usually performed jointly to save resources and reduce the error propagation in the pipeline.However, most of the existing joint models only focus on the benefits of encoder sharing between subtasks but ignore the difference.Therefore, we propose a joint ABSA model, which not only enjoys the benefits of encoder sharing but also focuses on the difference to improve the effectiveness of the model.In detail, we introduce a dual-encoder design, in which a pair encoder especially focuses on candidate aspect-opinion pair classification, and the original encoder keeps attention on sequence labeling.Empirical results show that our proposed model shows robustness and significantly outperforms the previous state-ofthe-art on four benchmark datasets.
Multi-task Learning to Enable Location Mention Identification in the Early Hours of a Crisis Event

Training a robust and reliable deep learning model requires a large amount of data.In the crisis domain, building deep learning models to identify actionable information from the huge influx of data posted by eyewitnesses of crisis events on social media, in a time-critical manner, is central for fast response and relief operations.However, building a large, annotated dataset to train deep learning models is not always feasible in a crisis situation.In this paper, we investigate a multi-task learning approach to concurrently leverage available annotated data for several related tasks from the crisis domain to improve the performance on a main task with limited annotated data.Specifically, we focus on using multi-task learning to improve the performance on the task of identifying location mentions in crisis tweets.
Enhancing Diagnosis Through Technology

Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care unit environments. The field needs well-designed studies to identify the most effective CDS approaches. Evolving artificial intelligence and machine learning models may reduce information-overload and enable teams to take better advantage of the large volume of patient data available to them. It is vital to effectively integrate new CDS into clinical workflows and to align closely with the cognitive processes of frontline clinicians.
Optical In-Process Measurement: Concepts for Precise, Fast and Robust Optical Metrology for Complex Measurement Situations

Optical metrology is a key element for many areas of modern production. Preferably, measurements should take place within the production line (in-process) and keep pace with production speed, even if the parts have a complex geometry or are difficult to access. The challenge for modern optical in-process measurements is, therefore, how to simultaneously make optical metrology precise, fast, robust and capable of handling geometrical complexity. The potential of individual techniques to achieve these demands can be visualized by the tetrahedron of optical metrology. Depending on the application, techniques based on interferometry or geometrical optics may have to be preferred. The paper emphasizes complexity and robustness as prime areas of improvement. Concerning interferometric techniques, we report on fast acquisition as used in holography, tailoring of coherence properties and use of Multiple simultaneous Viewing direction holography (MultiView), self reference used in Computational Shear Interferometry (CoSI) and the simultaneous use of several light sources in Multiple Aperture Shear Interferometry (MArS) based on CoSI as these techniques have proven to be particularly effective. The use of advanced approaches based on CoSI requires a transition of the description of light from the use of the well-known wave field to the coherence function of light. Techniques based on geometric optics are generally comparatively robust against environmental disturbances, and Fringe Projection (FP) is shown to be especially useful in very demanding measurement conditions.
Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension

How can we generate concise explanations for multi-hop Reading Comprehension (RC)?The current strategies of identifying supporting sentences can be seen as an extractive questionfocused summarization of the input text.However, these extractive explanations are not necessarily concise i.e. not minimally sufficient for answering a question.Instead, we advocate for an abstractive approach, where we propose to generate a question-focused, abstractive summary of input paragraphs and then feed it to an RC system.Given a limited amount of human-annotated abstractive explanations, we train the abstractive explainer in a semi-supervised manner, where we start from the supervised model and then train it further through trial and error maximizing a conciseness-promoted reward function.Our experiments demonstrate that the proposed abstractive explainer can generate more compact explanations than an extractive explainer with limited supervision (only 2k instances) while maintaining sufficiency.1 Our implementation is publicly
Hyperbolic Hierarchy-Aware Knowledge Graph Embedding for Link Prediction

Knowledge graph embedding (KGE) using low-dimensional representations to predict missing information is widely applied in knowledge completion.Existing embedding methods are mostly built on Euclidean space, which are difficult to handle hierarchical structures.Hyperbolic embedding methods have shown the promise of high fidelity and concise representation for hierarchical data.However, the logical patterns in knowledge graphs are not considered well in these methods.To address this problem, we propose a novel KGE model with extended Poincaré Ball and polar coordinate system to capture hierarchical structures.We use the tangent space and exponential transformation to initialize and map the corresponding vectors to the Poincaré Ball in hyperbolic space.To solve the boundary conditions, the boundary is stretched and zoomed by expanding the modulus length in the Poincaré Ball.We optimize our model using polar coordinate and changing operators in the extended Poincaré Ball.Experiments achieve new state-of-the-art results on part of link prediction tasks, which demonstrates the effectiveness of our method.
Investigating Pretrained Language Models for Graph-to-Text Generation

Graph-to-text generation aims to generate fluent texts from graph-based data. In this paper, we investigate two recent pretrained language models (PLMs) and analyze the impact of different task-adaptive pretraining strategies for PLMs in graph-to-text generation. We present a study across three graph domains: meaning representations, Wikipedia knowledge graphs (KGs) and scientific KGs. We show that approaches based on PLMs BART and T5 achieve new state-of-the-art results and that task-adaptive pretraining strategies improve their performance even further. We report new state-of-the-art BLEU scores of 49.72 on AMR-LDC2017T10, 59.70 on WebNLG, and 25.66 on AGENDA datasets - a relative improvement of 31.8%, 4.5%, and 42.4%, respectively, with our models generating significantly more fluent texts than human references. In an extensive analysis, we identify possible reasons for the PLMs’ success on graph-to-text tasks. Our findings suggest that the PLMs benefit from similar facts seen during pretraining or fine-tuning, such that they perform well even when the input graph is reduced to a simple bag of node and edge labels.
Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval

Recent work has shown that dense passage retrieval techniques achieve better ranking accuracy in open-domain question answering compared to sparse retrieval techniques such as BM25, but at the cost of large space and memory requirements.In this paper, we analyze the redundancy present in encoded dense vectors and show that the default dimension of 768 is unnecessarily large.To improve space efficiency, we propose a simple unsupervised compression pipeline that consists of principal component analysis (PCA), product quantization, and hybrid search.We further investigate other supervised baselines and find surprisingly that unsupervised PCA outperforms them in some settings.We perform extensive experiments on five question answering datasets and demonstrate that our best pipeline achieves good accuracy-space trade-offs, for example, 48× compression with less than 3% drop in top-100 retrieval accuracy on average or 96× compression with less than 4% drop.
The Role of Linguistic Typology, Target Language Proficiency and Prototypes in Learning Aspectual Contrasts in Italian as Additional Language

The study examines how prototypes and typological relationships between the L1, the L2 and the target language (TL) interact with TL proficiency in learning Italian as additional language. Low-proficiency and high-proficiency undergraduate learners of Italian (N = 25) with Swedish as L1 performed an oral retelling story test, aiming to elicit the Italian aspectual contrast perfective-imperfective. Their tense selection was analyzed considering the predicates’ lexical aspect and the learners’ knowledge of a Romance L2, or lack thereof. The findings show that the typological proximity between the L2 and the TL exerts a differential role depending on TL proficiency. Initially, it is beneficial for accelerating the overall emergence of the imperfetto as an aspectual marker. However, the prototype factor and, more specifically, the predicates’ dynamicity influences the selection of past inflectional morphology. At more advanced stages, knowledge of a Romance language helps learners move beyond prototypical associations with the passato prossimo, but it does not seem to influence the use of the imperfetto among high-proficiency learners. These results are discussed in the light of research on the second and additional language learning of aspectual contrasts in Romance languages.
Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems

In this paper, we show how a multi-class grammatical error detection (GED) system can be used to improve grammatical error correction (GEC) for English.Specifically, we first develop a new state-of-the-art binary detection system based on pre-trained ELECTRA, and then extend it to multi-class detection using different error type tagsets derived from the ERRANT framework.Output from this detection system is used as auxiliary input to finetune a novel encoder-decoder GEC model, and we subsequently re-rank the N -best GEC output to find the hypothesis that most agrees with the GED output.Results show that fine-tuning the GEC system using 4-class GED produces the best model, but re-ranking using 55-class GED leads to the best performance overall.This suggests that different multi-class GED systems benefit GEC in different ways.Ultimately, our system outperforms all other previous work that combines GED and GEC, and achieves a new single-model NMT-based state of the art on the BEA-test benchmark.
Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity

Multi-task auxiliary learning utilizes a set of relevant auxiliary tasks to improve the performance of a primary task.A common usage is to manually select multiple auxiliary tasks for multi-task learning on all data, which raises two issues: (1) selecting beneficial auxiliary tasks for a primary task is nontrivial; (2) when the auxiliary datasets are large, training on all data becomes time-expensive and impractical.Therefore, this paper focuses on addressing these problems and proposes a timeefficient sampling method to select the data that is most relevant to the primary task.The proposed method allows us to only train on the most beneficial sub-datasets from the auxiliary tasks, achieving efficient multi-task auxiliary learning.The experiments on three benchmark datasets (RTE, MRPC, STS-B) show that our method significantly outperforms random sampling and ST-DNN.Also, by applying our method, the model can surpass fully-trained MT-DNN on RTE, MRPC, STS-B, using only 50%, 66%, and 1% of data, respectively. 1
Point-to-Point Path Planning Based on User Guidance and Screw Linear Interpolation

Abstract Despite the increasing number of collaborative robots in human-centered manufacturing, currently, industrial robots are still largely preprogrammed with very little autonomous features. In this context, it is paramount that the robot planning and motion generation strategies are able to account for changes in production line in a timely and easy-to-implement fashion. The same requirements are also valid for service robotics in unstructured environments where an explicit definition of a task and the underlying path and constraints are often hard to characterize. In this regard, this paper presents a real-time point-to-point kinematic task-space planner based on screw interpolation that implicitly follows the underlying geometric constraints from a user demonstration. We demonstrate through example scenarios that implicit task constraints in a single user demonstration can be captured in our approach. It is important to highlight that the proposed planner does not learn a trajectory or intends to imitate a human trajectory, but rather explores the geometric features throughout a one-time guidance and extend such features as constraints in a generalized path generator. In this sense, the framework allows for generalization of initial and final configurations, it accommodates path disturbances, and it is agnostic to the robot being used. We evaluate our approach on the 7 DOF Baxter robot on a multitude of common tasks and also show generalization ability of our method with respect to different conditions.
RDD-Eclat: Approaches to Parallelize Eclat Algorithm on Spark RDD Framework

Initially, a number of frequent itemset mining (FIM) algorithms have been designed on the Hadoop MapReduce, a distributed big data processing framework. But, due to heavy disk I/O, MapReduce is found to be inefficient for such highly iterative algorithms. Therefore, Spark, a more efficient distributed data processing framework, has been developed with in-memory computation and resilient distributed dataset (RDD) features to support the iterative algorithms. On the Spark RDD framework, Apriori and FP-Growth based FIM algorithms have been designed, but Eclat-based algorithm has not been explored yet. In this paper, RDD-Eclat, a parallel Eclat algorithm on the Spark RDD framework is proposed with its five variants. The proposed algorithms are evaluated on the various benchmark datasets, which shows that RDD-Eclat outperforms the Spark-based Apriori by many times. Also, the experimental results show the scalability of the proposed algorithms on increasing the number of cores and size of the dataset.
Real-Time Monitoring of COVID-19 SOP in Public Gathering Using Deep Learning Technique

Crowd management has attracted serious attention under the prevailing pandemic conditions of COVID-19, emphasizing that sick persons do not become a source of virus transmission. World Health Organization (WHO) guidelines include maintaining a safe distance and wearing a mask in gatherings as part of standard operating procedures (SOP), considered thus far the most effective preventive measures to protect against COVID-19. Several methods and strategies have been used to construct various face detection and social distance detection models. In this paper, a deep learning model is presented to detect people without masks and those not keeping a safe distance to contain the virus. It also counts individuals who violate the SOP. The proposed model employs the Single Shot Multi-box Detector as a feature extractor, followed by Spatial Pyramid Pooling (SPP) to integrate the extracted features to improve the model's detecting capabilities. The MobilenetV2 architecture as a framework for the classifier makes the model highly light, fast, and computationally efficient, allowing it to be employed in embedded devices to do real-time mask and social distance detection, which is the sole objective of this research. This paper's technique yields an accuracy score of 99% and reduces the loss to 0.04%. Doi: 10.28991/esj-2021-SPER-14 Full Text: PDF
Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts

Short text nowadays has become a more fashionable form of text data, e.g., Twitter posts, news titles, and product reviews.Extracting semantic topics from short texts plays a significant role in a wide spectrum of NLP applications, and neural topic modeling is now a major tool to achieve it.Motivated by learning more coherent and semantic topics, in this paper we develop a novel neural topic model named Dual Word Graph Topic Model (DWGTM), which extracts topics from simultaneous word co-occurrence and semantic correlation graphs.To be specific, we learn word features from the global word cooccurrence graph, so as to ingest rich word co-occurrence information; we then generate text features with word features, and feed them into an encoder network to get topic proportions per-text; finally, we reconstruct texts and word co-occurrence graph with topical distributions and word features, respectively.Besides, to capture semantics of words, we also apply word features to reconstruct a word semantic correlation graph computed by pretrained word embeddings.Upon those ideas, we formulate DWGTM in an auto-encoding paradigm and efficiently train it with the spirit of neural variational inference.Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models.
AMuSE-WSD: An All-in-one Multilingual System for Easy Word Sense Disambiguation

Over the past few years, Word Sense Disambiguation (WSD) has received renewed interest: recently proposed systems have shown the remarkable effectiveness of deep learning techniques in this task, especially when aided by modern pretrained language models.Unfortunately, such systems are still not available as ready-to-use end-to-end packages, making it difficult for researchers to take advantage of their performance.The only alternative for a user interested in applying WSD to downstream tasks is to use currently available endto-end WSD systems, which, however, still rely on graph-based heuristics or non-neural machine learning algorithms.In this paper, we fill this gap and propose AMuSE-WSD, the first end-to-end system to offer high-quality sense information in 40 languages through a state-of-the-art neural model for WSD.We hope that AMuSE-WSD will provide a stepping stone for the integration of meaning into real-world applications and encourage further studies in lexical semantics.
A Novel Wikipedia based Dataset for Monolingual and Cross-Lingual Summarization

Cross-lingual summarization is a challenging task for which there are no cross-lingual scientific resources currently available.To overcome the lack of a high-quality resource, we present a new dataset for monolingual and cross-lingual summarization considering the English-German pair.We collect high-quality, real-world cross-lingual data from Spektrum der Wissenschaft, which publishes humanwritten German scientific summaries of English science articles on various subjects.The generated Spektrum dataset is small; therefore, we harvest a similar dataset from the Wikipedia Science Portal to complement it.The Wikipedia dataset consists of English and German articles, which can be used for monolingual and cross-lingual summarization.Furthermore, we present a quantitative analysis of the datasets and results of empirical experiments with several existing extractive and abstractive summarization models.The results suggest the viability and usefulness of the proposed dataset for monolingual and crosslingual summarization.
Findings of the LoResMT 2021 Shared Task on COVID and Sign Language for Low-resource Languages

We present the findings of the LoResMT 2021 shared task which focuses on machine translation (MT) of COVID-19 data for both low-resource spoken and sign languages. The organization of this task was conducted as part of the fourth workshop on technologies for machine translation of low resource languages (LoResMT). Parallel corpora is presented and publicly available which includes the following directions: English↔Irish, English↔Marathi, and Taiwanese Sign language↔Traditional Chinese. Training data consists of 8112, 20933 and 128608 segments, respectively. There are additional monolingual data sets for Marathi and English that consist of 21901 segments. The results presented here are based on entries from a total of eight teams. Three teams submitted systems for English↔Irish while five teams submitted systems for English↔Marathi. Unfortunately, there were no systems submissions for the Taiwanese Sign language↔Traditional Chinese task. Maximum system performance was computed using BLEU and follow as 36.0 for English–Irish, 34.6 for Irish–English, 24.2 for English–Marathi, and 31.3 for Marathi–English.
Secured Perimeter with Electromagnetic Detection and Tracking with Drone Embedded and Static Cameras

Perimeter detection systems detect intruders penetrating protected areas, but modern solutions require the combination of smart detectors, information networks and controlling software to reduce false alarms and extend detection range. The current solutions available to secure a perimeter (infrared and motion sensors, fiber optics, cameras, radar, among others) have several problems, such as sensitivity to weather conditions or the high failure alarm rate that forces the need for human supervision. The system exposed in this paper overcomes these problems by combining a perimeter security system based on CEMF (control of electromagnetic fields) sensing technology, a set of video cameras that remain powered off except when an event has been detected. An autonomous drone is also informed where the event has been initially detected. Then, it flies through computer vision to follow the intruder for as long as they remain within the perimeter. This paper covers a detailed view of how all three components cooperate in harmony to protect a perimeter effectively, without having to worry about false alarms, blinding due to weather conditions, clearance areas, or privacy issues. The system also provides extra information of where the intruder is or has been, at all times, no matter whether they have become mixed up with more people or not during the attack.
Grammatical Error Correction with Contrastive Learning in Low Error Density Domains

Although grammatical error correction (GEC) has achieved good performance on texts written by learners of English as a second language, performance on low error density domains where texts are written by English speakers of varying levels of proficiency can still be improved.In this paper, we propose a contrastive learning approach to encourage the GEC model to assign a higher probability to a correct sentence while reducing the probability of incorrect sentences that the model tends to generate, so as to improve the accuracy of the model.Experimental results show that our approach significantly improves the performance of GEC models in low error density domains, when evaluated on the benchmark CWEB dataset.