Reviewerly/exHarmony huggingface dataset:
Graph convolutional and attention models for entity classification in multilayer networks

Abstract Graph Neural Networks (GNNs) are powerful tools that are nowadays reaching state of the art performances in a plethora of different tasks such as node classification, link prediction and graph classification. A challenging aspect in this context is to redefine basic deep learning operations, such as convolution, on graph-like structures, where nodes generally have unordered neighborhoods of varying size. State-of-the-art GNN approaches such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) work on monoplex networks only, i.e., on networks modeling a single type of relation among an homogeneous set of nodes. The aim of this work is to generalize such approaches by proposing a GNN framework for representation learning and semi-supervised classification in multilayer networks with attributed entities, and arbitrary number of layers and intra-layer and inter-layer connections between nodes. We instantiate our framework with two new formulations of GAT and GCN models, namely and , specifically devised for general, attributed multilayer networks. The proposed approaches are evaluated on an entity classification task on nine widely used real-world network datasets coming from different domains and with different structural characteristics. Results show that both our proposed and methods provide effective and efficient solutions to the problem of entity classification in multilayer attributed networks, being faster to learn and offering better accuracy than the competitors. Furthermore, results show how our methods are able to take advantage of the presence of real attributes for the entities, in addition to arbitrary inter-layer connections between the nodes in the various layers.
Heterogeneous Graph Neural Networks for Keyphrase Generation

The encoder-decoder framework achieves stateof-the-art results in keyphrase generation (KG) tasks by predicting both present keyphrases that appear in the source document and absent keyphrases that do not.However, relying solely on the source document can result in generating uncontrollable and inaccurate absent keyphrases.To address these problems, we propose a novel graph-based method that can capture explicit knowledge from related references.Our model first retrieves some document-keyphrases pairs similar to the source document from a pre-defined index as references.Then a heterogeneous graph is constructed to capture relationships of different granularities between the source document and its references.To guide the decoding process, a hierarchical attention and copy mechanism is introduced, which directly copies appropriate words from both the source document and its references based on their relevance and significance.The experimental results on multiple KG benchmarks show that the proposed model achieves significant improvements against other baseline models, especially with regard to the absent keyphrase prediction.
A Corpus for Multilingual Analysis of Online Terms of Service

We present the first annotated corpus for multilingual analysis of potentially unfair clauses in online Terms of Service.The data set comprises a total of 100 contracts, obtained from 25 documents annotated in four different languages: English, German, Italian, and Polish.For each contract, potentially unfair clauses for the consumer are annotated, for nine different unfairness categories.We show how a simple yet efficient annotation projection technique based on sentence embeddings could be used to automatically transfer annotations across languages.
Measuring Sentence-Level and Aspect-Level (Un)certainty in Science Communications

Certainty and uncertainty are fundamental to science communication.Hedges have widely been used as proxies for uncertainty.However, certainty is a complex construct, with authors expressing not only the degree but the type and aspects of uncertainty in order to give the reader a certain impression of what is known.Here, we introduce a new study of certainty that models both the level and the aspects of certainty in scientific findings.Using a new dataset of 2167 annotated scientific findings, we demonstrate that hedges alone account for only a partial explanation of certainty.We show that both the overall certainty and individual aspects can be predicted with pre-trained language models, providing a more complete picture of the author's intended communication.Downstream analyses on 431K scientific findings from news and scientific abstracts demonstrate that modeling sentencelevel and aspect-level certainty is meaningful for areas like science communication.Both the model and datasets used in this paper are released at https://blablablab.si.umich.edu/projects/certainty/
Towards a Consistent Interpretation of AIOps Models

Artificial Intelligence for IT Operations (AIOps) has been adopted in organizations in various tasks, including interpreting models to identify indicators of service failures. To avoid misleading practitioners, AIOps model interpretations should be consistent (i.e., different AIOps models on the same task agree with one another on feature importance). However, many AIOps studies violate established practices in the machine learning community when deriving interpretations, such as interpreting models with suboptimal performance, though the impact of such violations on the interpretation consistency has not been studied. In this article, we investigate the consistency of AIOps model interpretation along three dimensions: internal consistency, external consistency, and time consistency. We conduct a case study on two AIOps tasks: predicting Google cluster job failures and Backblaze hard drive failures. We find that the randomness from learners, hyperparameter tuning, and data sampling should be controlled to generate consistent interpretations. AIOps models with AUCs greater than 0.75 yield more consistent interpretation compared to low-performing models. Finally, AIOps models that are constructed with the Sliding Window or Full History approaches have the most consistent interpretation with the trends presented in the entire datasets. Our study provides valuable guidelines for practitioners to derive consistent AIOps model interpretation.
A Context-Aware Language Model to Improve the Speech Recognition in Air Traffic Control

Recognizing isolated digits of the flight callsign is an important and challenging task for automatic speech recognition (ASR) in air traffic control (ATC). Fortunately, the flight callsign is a kind of prior ATC knowledge and is available from dynamic contextual information. In this work, we attempt to utilize this prior knowledge to improve the performance of the callsign identification by integrating it into the language model (LM). The proposed approach is named context-aware language model (CALM), which can be applied for both the ASR decoding and rescoring phase. The proposed model is implemented with an encoder–decoder architecture, in which an extra context encoder is proposed to consider the contextual information. A shared embedding layer is designed to capture the correlations between the ASR text and contextual information. The context attention is introduced to learn discriminative representations to support the decoder module. Finally, the proposed approach is validated with an end-to-end ASR model on a multilingual real-world corpus (ATCSpeech). Experimental results demonstrate that the proposed CALM outperforms other baselines for both the ASR and callsign identification task, and can be practically migrated to a real-time environment.
Understanding How Programmers Can Use Annotations on Documentation

Modern software development requires developers to find and effectively utilize new APIs and their documentation, but documentation has many well-known issues. Despite this, developers eventually overcome these issues but have no way of sharing what they learned. We investigate sharing this documentation-specific information through annotations, which have advantages over developer forums as the information is contextualized, not disruptive, and is short, thus easy to author. Developers can also author annotations to support their own comprehension. In order to support the documentation usage behaviors we found, we built the Adamite annotation tool, which provides features such as multiple anchors, annotation types, and pinning. In our user study, we found that developers are able to create annotations that are useful to themselves and are able to utilize annotations created by other developers when learning a new API, with readers of the annotations completing 67% more of the task, on average, than the baseline.
Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation

Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR.However, this assumes a high quality of generated AMRs, potentially limiting the transferability to the target task.In this paper, we investigate different techniques for automatically generating AMR annotations, where we aim to study which source of information yields better multilingual results.Our models trained on gold AMR with silver (machine translated) sentences outperform approaches which leverage generated silver AMR.We find that combining both complementary sources of information further improves multilingual AMR-to-text generation.Our models surpass the previous state of the art for German, Italian, Spanish, and Chinese by a large margin. 1
“Please Let me Use Google Translate”: Thai EFL Students’ Behavior and Attitudes toward Google Translate Use in English Writing

The present study aims to investigate how Thai EFL university students use Google Translate (GT) in English writing, how they post-edit (PE) its outputs, and how they view GT use in English writing. The participants were 15 third-year non-English major students from three universities in Thailand. The data collection tools were an interview and two writing assignments. After the data analysis, the findings revealed the students&amp;rsquo; behavior of GT use and their output PE as well as their attitudes toward GT use in English writing. The results reported the students always used GT in completing writing tasks at both sentence and paragraph levels, and most students did PE the outputs before applying them. However, a few students used the outputs with no PE because they trusted in GT more than they did in themselves. Regarding the PE level, the students intended to address lexical and syntax errors, so their correcting covered the light level. The results also revealed mixed messages in their attitudes toward GT use in English writing. Most students viewed GT as a helpful, reliable assistant enhancing their writing quality, but some raw GT outputs of phrases, idioms, long sentences, and paragraphs were found incomprehensible. Also, the students acquired some bad habits from using GT. However, most students disagreed with not being allowed to use GT in English writing. The study recommended language teachers to provide Thai EFL students adequate instructions for the effective use of GT and its output PE.&#x0D; &amp;nbsp;
Modification of the Role of a Teacher Under the Conditions of Distance Learning

The article examines the features of the transformation of the role of a teacher in higher education in connection with the digitalization of education in the period before the pandemic, and during the transition of the university to a forced remote mode under the conditions of the pandemic. The assessment of the effectiveness of distance learning from the point of view of students and teachers is given. The purpose of the article is to determine expectations, the degree and nature of the influence of the teacher's personality on the effectiveness of educational process in general and distance learning in particular. Researching the effectiveness of higher education in the digital age and assessing the impact on teacher effectiveness involves the use of a number of scientific principles and research methods, theoretical and empirical. To achieve the goal, the scientific works of leading foreign and Russian scientists on the problems of digitalization of education, dehumanization of education, transformation of the role of the teacher have been analyzed, and our own research has been carried out on the basis of the Moscow Polytechnic University in the form of a survey. An assessment of the emotional consequences of isolation is given. The analysis of the effectiveness of distance learning, from the point of view of higher education teachers and students has been carried out. The expectations of students and the change in the role of a teacher of higher education as a result of expansion of functional responsibilities under the circumstances of distance learning are described. Long before the coronavirus pandemic, digitalization had been a trend in the education system. A year of distance learning has revolutionized higher education. The role of a teacher in higher education has been transformed with a significant expansion of functions and a rapid increase in the level of competencies.
VRChem: A Virtual Reality Molecular Builder

Virtual reality provides a powerful way to visualize the three-dimensional, atomic-level structures of molecules and materials. We present new virtual reality software for molecular modeling and for testing the use of virtual reality in organic chemistry education. The open-source software, named VRChem, was developed primarily for building, visualizing and manipulating organic molecules using a head-mounted virtual reality system. The design goal of the VRChem software was to create an easy-to-use and entertaining user experience for molecular modeling in virtual reality. We discuss the design and implementation of VRChem, together with real-life user experiences collected from students and academic research staff.
Blockchain-Based Context-Aware Authorization Management as a Service in IoT

Internet of Things (IoT) applications bring evolved and intelligent services that can help improve users' daily lives. These applications include home automation, health care, and smart agriculture. However, IoT development and adoption face various security and privacy challenges that need to be overcome. As a promising security paradigm, context-aware security enables one to enforce security and privacy mechanisms adaptively. Moreover, with the advancements in edge computing, context-aware security services can dynamically be placed close to a user's location and enable the support of low latency communication and mobility. Therefore, the design of an adaptive and decentralized access control mechanism becomes a necessity. In this paper, we propose a decentralized context-aware authorization management as a service based on the blockchain. The proposed architecture extends the Authentication and Authorization for Constrained Environments (ACE) framework with blockchain technology and context-awareness capabilities. Instead of a classic Open Authorization 2.0 (OAuth) access token, it uses a new contextual access token. The evaluation results show our proposition's effectiveness and advantages in terms of usability, security, low latency, and energy consumption.
A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base

Knowledge Base Question Answering (KBQA) is to answer natural language questions posed over knowledge bases (KBs).This paper targets at empowering the IR-based KBQA models with the ability of numerical reasoning for answering ordinal constrained questions.A major challenge is the lack of explicit annotations about numerical properties.To address this challenge, we propose a pretraining numerical reasoning model consisting of NumGNN and NumTransformer, guided by explicit self-supervision signals.The two modules are pretrained to encode the magnitude and ordinal properties of numbers respectively and can serve as model-agnostic plugins for any IR-based KBQA model to enhance its numerical reasoning ability.Extensive experiments on two KBQA benchmarks verify the effectiveness of our method to enhance the numerical reasoning ability for IR-based KBQA models.Our code and datasets are available online 1 .
3D hierarchical dual-attention fully convolutional networks with hybrid losses for diverse glioma segmentation

Accurate glioma segmentation based on magnetic resonance imaging (MRI) is crucial for assisting with the diagnosis of gliomas. However, the manual delineation of all diverse gliomas, including the whole tumors (WTs), tumor cores (TCs) and enhancing tumors (ETs) of high-grade gliomas (HGG) and low-grade gliomas (LGG), is laborious and often error prone. The different phenotypes, sizes and locations of gliomas in/between patients make automatic segmentation a challenging task. To alleviate these challenges, in this paper, we propose a 3D fully convolutional network (FCN) with a dual-attention (i.e., global and local attention) mechanism to segment diverse gliomas simultaneously. The global attention mechanism (GAM) focuses on segmenting gliomas precisely by segment discrimination learning with a weight-allocated segmentation loss function to alleviate biased results obtained for tumors with large sizes and an adversarial loss function to refine the segmentations of areas with low contrast relative to their neighbors. The local attention mechanism (LAM) constantly revises effective features with the guidance of a united loss function at different levels. Furthermore, we present a hierarchical feature module (HFM) with a weight-sharing block to obtain more information about the boundaries of different scales, aiming at enhancing the learning of ambiguous tumor outlines. According to experimental results, our network outperforms ten state-of-the-art methods. Ablation studies show that the proposed model components are effective for diverse glioma segmentation.
Self-Supervised Curriculum Learning for Spelling Error Correction

Spelling Error Correction (SEC) that requires high-level language understanding is a challenging but useful task.Current SEC approaches normally leverage a pre-training then fine-tuning procedure that treats data equally.By contrast, Curriculum Learning (CL) utilizes training data differently during training and has shown its effectiveness in improving both performance and training efficiency in many other NLP tasks.In NMT, a model's performance has been shown sensitive to the difficulty of training examples, and CL has been shown effective to address this.In SEC, the data from different language learners are naturally distributed at different difficulty levels (some errors made by beginners are obvious to correct while some made by fluent speakers are hard), and we expect that designing a curriculum correspondingly for model learning may also help its training and bring about better performance.In this paper, we study how to further improve the performance of the state-of-the-art SEC method with CL, and propose a Self-Supervised Curriculum Learning (SSCL) approach.Specifically, we directly use the cross-entropy loss as criteria for: 1) scoring the difficulty of training data, and 2) evaluating the competence of the model.In our approach, CL improves the model training, which in return improves the CL measurement.In our experiments on the SIGHAN 2015 Chinese spelling check task, we show that SSCL is superior to previous norm-based and uncertainty-aware approaches, and establish a new state of the art (74.38%F1).
Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport

Timeline Summarization identifies major events from a news collection and describes them following temporal order, with key dates tagged.Previous methods generally generate summaries separately for each date after they determine the key dates of events.These methods overlook the events' intra-structures (arguments) and inter-structures (event-event connections).Following a different route, we propose to represent the news articles as an event-graph, thus the summarization task becomes compressing the whole graph to its salient sub-graph.The key hypothesis is that the events connected through shared arguments and temporal order depict the skeleton of a timeline, containing events that are semantically related, structurally salient, and temporally coherent in the global event graph.A time-aware optimal transport distance is then introduced for learning the compression model in an unsupervised manner.We show that our approach significantly improves the state of the art on three real-world datasets, including two public standard benchmarks and our newly collected Timeline 100 dataset. 1
Effectively Leveraging BERT for Legal Document Classification

Bidirectional Encoder Representations from Transformers (BERT) has achieved state-of-the-art performances on several text classification tasks, such as GLUE and sentiment analysis. Recent work in the legal domain started to use BERT on tasks, such as legal judgement prediction and violation prediction. A common practise in using BERT is to fine-tune a pre-trained model on a target task and truncate the input texts to the size of the BERT input (e.g. at most 512 tokens). However, due to the unique characteristics of legal documents, it is not clear how to effectively adapt BERT in the legal domain. In this work, we investigate how to deal with long documents, and how is the importance of pre-training on documents from the same domain as the target task. We conduct experiments on the two recent datasets: ECHR Violation Dataset and the Overruling Task Dataset, which are multi-label and binary classification tasks, respectively. Importantly, on average the number of tokens in a document from the ECHR Violation Dataset is more than 1,600. While the documents in the Overruling Task Dataset are shorter (the maximum number of tokens is 204). We thoroughly compare several techniques for adapting BERT on long documents and compare different models pre-trained on the legal and other domains. Our experimental results show that we need to explicitly adapt BERT to handle long documents, as the truncation leads to less effective performance. We also found that pre-training on the documents that are similar to the target task would result in more effective performance on several scenario.
GenerativeRE: Incorporating a Novel Copy Mechanism and Pretrained Model for Joint Entity and Relation Extraction

Previous neural seq2seq models have shown the effectiveness for jointly extracting relation triplets.However, most of these models suffer from incompletion and disorder problems when they extract multi-token entities from input sentences.To tackle these problems, we propose a generative, multi-task learning framework, named GenerativeRE.We firstly propose a special entity labelling method on both input and output sequences.During the training stage, GenerativeRE fine-tunes the pretrained generative model and learns the special entity labels simultaneously.During the inference stage, we propose a novel copy mechanism equipped with three mask strategies, to generate the most probable tokens by diminishing the scope of the model decoder.Experimental results show that our model achieves 4.6% and 0.9% F1 score improvements over the current state-of-the-art methods in the NYT24 and NYT29 benchmark datasets respectively.