Reviewerly/exHarmony huggingface dataset:
DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering

In this paper, we propose a novel model called Adversarial Multi-Task Network (AMTN) for jointly modeling Recognizing Question Entailment (RQE) and medical Question Answering (QA) tasks.AMTN utilizes a pre-trained BioBERT model and an Interactive Transformer to learn the shared semantic representations across different task through parameter sharing mechanism.Meanwhile, an adversarial training strategy is introduced to separate the private features of each task from the shared representations.Experiments on BioNLP 2019 RQE and QA shared task datasets show that our model benefits from the shared representations of both tasks provided by multi-task learning and adversarial training, and obtains significant improvements upon the singletask models.
Morphologically Annotated Corpora for Seven Arabic Dialects: Taizi, Sanaani, Najdi, Jordanian, Syrian, Iraqi and Moroccan

We present a collection of morphologically annotated corpora for seven Arabic dialects: Taizi Yemeni, Sanaani Yemeni, Najdi, Jordanian, Syrian, Iraqi and Moroccan Arabic.The corpora collectively cover over 200,000 words, and are all manually annotated in a common set of standards for orthography, diacritized lemmas, tokenization, morphological units and English glosses.These corpora will be publicly available to serve as benchmarks for training and evaluating systems for Arabic dialect morphological analysis and disambiguation.
Generating Highly Relevant Questions

The neural seq2seq based question generation (QG) is prone to generating generic and undiversified questions that are poorly relevant to the given passage and target answer.In this paper, we propose two methods to address the issue.(1) By a partial copy mechanism, we prioritize words that are morphologically close to words in the input passage when generating questions; (2) By a QA-based reranker, from the n-best list of question candidates, we select questions that are preferred by both the QA and QG model.Experiments and analyses demonstrate that the proposed two methods substantially improve the relevance of generated questions to passages and answers.
ParaQG: A System for Generating Questions and Answers from Paragraphs

Generating syntactically and semantically valid and relevant questions from paragraphs is useful with many applications.Manual generation is a labour-intensive task, as it requires the reading, parsing and understanding of long passages of text.A number of question generation models based on sequence-to-sequence techniques have recently been proposed.Most of them generate questions from sentences only, and none of them is publicly available as an easy-to-use service.In this paper, we demonstrate ParaQG, a Web-based system for generating questions from sentences and paragraphs.ParaQG incorporates a number of novel functionalities to make the question generation process user-friendly.It provides an interactive interface for a user to select answers with visual insights on generation of questions.It also employs various faceted views to group similar questions as well as filtering techniques to eliminate unanswerable questions.
The Swedish PoliGraph: A Semantic Graph for Argument Mining of Swedish Parliamentary Data

As part of a larger project on argument mining of Swedish parliamentary data, we have created a semantic graph that, together with named entity recognition and resolution (NER), should make it easier to establish connections between arguments in a given debate.The graph is essentially a semantic database that keeps track of Members of Parliament (MPs), in particular their presence in the parliament and activity in debates, but also party affiliation and participation in commissions.The hope is that the Swedish PoliGraph will enable us to perform named entity resolution on debates in the Swedish parliament with a high accuracy, with the aim of determining to whom an argument is directed.
hpGAT: High-Order Proximity Informed Graph Attention Network

Graph neural networks (GNNs) have recently made remarkable breakthroughs in the paradigm of learning with graph-structured data. However, most existing GNNs limit the receptive field of the node on each layer to its connected (one-hop) neighbors, which disregards the fact that large receptive field has been proven to be a critical factor in state-of-the-art neural networks. In this paper, we propose a novel approach to appropriately define a variable receptive field for GNNs by incorporating high-order proximity information extracted from the hierarchical topological structure of the input graph. Specifically, multiscale groups obtained from trainable hierarchical semi-nonnegative matrix factorization are used for adjusting the weights when aggregating one-hop neighbors. Integrated with the graph attention mechanism on attributes of neighboring nodes, the learnable parameters within the process of aggregation are optimized in an end-to-end manner. Extensive experiments show that the proposed method (hpGAT) outperforms state-of-the-art methods and demonstrate the importance of exploiting high-order proximity in handling noisy information of local neighborhood.
Comparative Evaluation of Node-Link and Sankey Diagrams for the Cyber Security Domain

Visualization tools are critical components of cyber security systems allowing analyzers to better understand, detect and prevent security breaches. Security administrators need to understand which users accessed the database and what operations were performed in order to detect irregularities. The current work compares the Sankey diagram with the more commonly used node-link diagram as an alternative visualization technique for cyber security tasks in a controlled experiment. The results indicate, that the Sankey tool showed a consistent advantage in task completion time and was more effective (measured by the percent of correct answers) in synoptic tasks, while the Node-link diagram was more effective in basic, elementary tasks. Further results revealed that performance had only a small effect on user satisfaction and preferences. Our results suggest that the Sankey tool may be a viable option for cyber security visualization tools and strengthens the need to provide personalized visualization tools based on user preferences.
Dhff: Robust Multi-Scale Person Search by Dynamic Hierarchical Feature Fusion

Person Search plays the role of the ultimate destination of person re-identification (re-ID) in real applications. It has many challenges that person re-ID doesn't need to handle, such as mis-detections, false alarms and multi-scale matching. In contrast to previous works, we show that a strong multi-scale person matching system can result in a good person search performance with a common deep object detector (e.g. Faster-RCNN). In this work, we provide a robust person search method called Dynamic Hierarchical Feature Fusion (DHFF) which is based on multi-level feature fusion to tackle with multi-scale matching. In addition, A Multi-Metric loss is proposed to train the model effectively and stably with numerous identities. We evaluate our method on two large person search benchmark data sets: CUHK-SYSU and PRW. Experiments show that the proposed algorithm outperforms other state-of-the-art person search methods.
Deep Copycat Networks for Text-to-Text Generation

Most text-to-text generation tasks, for example text summarisation and text simplification, require copying words from the input to the output.We introduce copycat, a transformerbased pointer network for such tasks which obtains competitive results in abstractive text summarisation and generates more abstractive summaries.We propose a further extension of this architecture for automatic post-editing, where generation is conditioned over two inputs (source language and machine translation), and the model is capable of deciding where to copy information from.This approach achieves competitive performance when compared to state-of-the-art automated post-editing systems.More importantly, we show that it addresses a well-known limitation of automatic post-editing -overcorrecting translations -and that our novel mechanism for copying source language words improves the results.
Phrase Grounding by Soft-Label Chain Conditional Random Field

The phrase grounding task aims to ground each entity mention in a given caption of an image to a corresponding region in that image.Although there are clear dependencies between how different mentions of the same caption should be grounded, previous structured prediction methods that aim to capture such dependencies need to resort to approximate inference or non-differentiable losses.In this paper, we formulate phrase grounding as a sequence labeling task where we treat candidate regions as potential labels, and use neural chain Conditional Random Fields (CRFs) to model dependencies among regions for adjacent mentions.In contrast to standard sequence labeling tasks, the phrase grounding task is defined such that there may be multiple correct candidate regions.To address this multiplicity of gold labels, we define socalled Soft-Label Chain CRFs, and present an algorithm that enables convenient end-toend training.Our method establishes a new state-of-the-art on phrase grounding on the Flickr30k Entities dataset.Analysis shows that our model benefits both from the entity dependencies captured by the CRF and from the soft-label training regime.Our code is available at github.com/liujch1998/SoftLabelCCRF
Similarity Based Auxiliary Classifier for Named Entity Recognition

The segmentation problem is one of the fundamental challenges associated with name entity recognition (NER) tasks that aim to reduce the boundary error when detecting a sequence of entity words.A considerable number of advanced approaches have been proposed and most of them exhibit performance deterioration when entities become longer.Inspired by previous work in which a multi-task strategy is used to solve segmentation problems, we design a similarity based auxiliary classifier (SAC), which can distinguish entity words from non-entity words.Unlike conventional classifiers, SAC uses vectors to indicate tags.Therefore, SAC can calculate the similarities between words and tags, and then compute a weighted sum of the tag vectors, which can be considered a useful feature for NER tasks.Empirical results are used to verify the rationality of the SAC structure and demonstrate the SAC model's potential in performance improvement against our baseline approaches.
Memory Grounded Conversational Reasoning

Seungwhan Moon, Pararth Shah, Rajen Subba, Anuj Kumar. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations. 2019.
CUNI System for the WMT19 Robustness Task

We present our submission to the WMT19 Robustness Task.Our baseline system is the Charles University (CUNI) Transformer system trained for the WMT18 shared task on News Translation.Quantitative results show that the CUNI Transformer system is already far more robust to noisy input than the LSTMbased baseline provided by the task organizers.We further improved the performance of our model by fine-tuning on the in-domain noisy data without influencing the translation quality on the news domain.
(Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus

We introduce unsupervised techniques based on phrase-based statistical machine translation for grammatical error correction (GEC) trained on a pseudo learner corpus created by Google Translation.We verified our GEC system through experiments on a low resource track of the shared task at Building Educational Applications 2019 (BEA2019).As a result, we achieved an F 0.5 score of 28.31 points with the test data.
JHU System Description for the MADAR Arabic Dialect Identification Shared Task

Our submission to the MADAR shared task on Arabic dialect identification (Bouamor et al., 2019) employed a language modeling technique called Prediction by Partial Matching, an ensemble of neural architectures, and sources of additional data for training word embeddings and auxiliary language models. 1 We found several of these techniques provided small boosts in performance, though a simple character-level language model was a strong baseline, and a lower-order LM achieved best performance on Subtask 2. Interestingly, word embeddings provided no consistent benefit, and ensembling struggled to outperform the best component submodel.This suggests the variety of architectures are learning redundant information, and future work may focus on encouraging decorrelated learning.
Machine Translation from an Intercomprehension Perspective

Within the first shared task on machine translation between similar languages, we present our first attempts on Czech to Polish machine translation from an intercomprehension perspective.We propose methods based on the mutual intelligibility of the two languages, taking advantage of their orthographic and phonological similarity, in the hope to improve over our baselines.The translation results are evaluated using BLEU.On this metric, none of our proposals could outperform the baselines on the final test set.The current setups are rather preliminary, and there are several potential improvements we can try in the future.
THE CONCEPT OF SMART CONTRACTS FROM THE LEGAL PERSPECTIVE

The purpose of article hereof is to introduce the significant characters of the smart contracts and certain ideas and proposals de lege ferenda on regulatory framework for smart contracts. Furthermore, present legislation with regard to the legal definition of the smart contract will be discussed from a comparative perspective. Particular note will be devoted to smart contracts in a relation to the contract law. Substantively, legal issues arising from the use of smart contracts, focussing upon actual and potential conflicts with established principles of contract law, will be introduced.
Zero-error quantum hypothesis testing in finite time with quantum error correction

Under the unitary evolutions it is always possible to distinguish different Hamiltonians with zero-error probability within a finite time. At the presence of noises, however, the error probability does not always go to zero within a finite time. In this article, we give a necessary and sufficient condition for Markovian dynamics to achieve zero-error quantum hypothesis testing in a finite time. We show that when the condition fails, the maximal fidelity of the output states under two different Hamiltonians, aided with arbitrary control operations, is always lower bounded by an exponential function, which remains positive at any finite time; zero-error quantum hypothesis testing thus cannot be achieved within any finite time. However, when the condition holds, quantum error corrections can be used to correct the noises and partially maintain the coherent evolutions, zero-error quantum hypothesis testing can then be achieved within a finite time.
Graph-Based Semi-Supervised Learning with Non-ignorable Non-response

Graph-based semi-supervised learning is a very powerful tool in classification tasks, while in most existing literature the labelled nodes are assumed to be randomly sampled. When the labelling status depends on the unobserved node response, ignoring the missingness can lead to significant estimation bias and handicap the classifiers. This situation is called non-ignorable non-response. To solve the problem, we propose a Graph-based joint model with Non-ignorable Non-response (GNN), followed by a joint inverse weighting estimation procedure incorporated with sampling imputation approach. Our method is proved to outperform some state-of-art models in both regression and classification problems, by simulations and real analysis on the Cora dataset.
Weakly Supervised Cross-lingual Semantic Relation Classification via Knowledge Distillation

Words in different languages rarely cover the exact same semantic space.This work characterizes differences in meaning between words across languages using semantic relations that have been used to relate the meaning of English words.However, because of translation ambiguity, semantic relations are not always preserved by translation.We introduce a cross-lingual relation classifier trained only with English examples and a bilingual dictionary.Our classifier relies on a novel attention-based distillation approach to account for translation ambiguity when transferring knowledge from English to crosslingual settings.On new English-Chinese and English-Hindi test sets, the resulting models largely outperform baselines that more naïvely rely on bilingual embeddings or dictionaries for cross-lingual transfer, and approach the performance of fully supervised systems on English tasks.
Restoration of turbulence-degraded images based on deep convolutional network

Atmospheric turbulence is an irregular form of motion in the atmosphere. Because of turbulence interference, when the optical system through the atmosphere of the target imaging, the observed image will appear point intensity diffusion, image blur, image drift and other turbulence effects. Digital recovery of the turbulence-degraded images technique is a classical ill-conditioned problem by removing the blurring effect and suppressing the noise. Traditional approaches relying on image heuristics suffer from high frequency noise amplification and processing artifacts. In this paper, the image degradation models of the turbulent flow are given, the point spread function of turbulence is simulated by the similar Gaussian function model, and investigated a general framework of neural networks for restoring turbulence-degraded images. The blur and additive noise are considered simultaneously. Two solutions respectively exploiting fully convolutional networks (FCN) and conditional Generative Adversarial Networks (CGAN) are presented. The FCN based on minimizing the mean squared reconstruction error (MSE) in pixel space gets high PSNR. On the other side, the CGAN based on perceptual loss optimization criterion retrieves more textures. We conduct comparison experiments to demonstrate the performance at different degree of turbulence intensity from the training configuration. The results indicate that the proposed networks outperform traditional approaches for restoring high frequency details and suppressing noise effectively.
CSE_E 1.0: An Integrated Automated Theorem Prover for First-Order Logic

First-order logic is an important part of mathematical logic, and automated theorem proving is an interdisciplinary field of mathematics and computer science. The paper presents an automated theorem prover for first-order logic, called C S E _ E 1.0, which is a combination of two provers contradiction separation extension (CSE) and E, where CSE is based on the recently-introduced multi-clause standard contradiction separation (S-CS) calculus for first-order logic and E is the well-known equational theorem prover for first-order logic based on superposition and rewriting. The motivation of the combined prover C S E _ E 1.0 is to (1) evaluate the capability, applicability and generality of C S E _ E , and (2) take advantage of novel multi-clause S-CS dynamic deduction of CSE and mature equality handling of E to solve more and harder problems. In contrast to other improvements of E, C S E _ E 1.0 optimizes E mainly from the inference mechanism aspect. The focus of the present work is given to the description of C S E _ E including its S-CS rule, heuristic strategies, and the S-CS dynamic deduction algorithm for implementation. In terms of combination, in order not to lose the capability of E and use C S E _ E to solve some hard problems which are unsolved by E, C S E _ E 1.0 schedules the running of the two provers in time. It runs plain E first, and if E does not find a proof, it runs plain CSE, then if it does not find a proof, some clauses inferred in the CSE run as lemmas are added to the original clause set and the combined clause set handed back to E for further proof search. C S E _ E 1.0 is evaluated through benchmarks, e.g., CASC-26 (2017) and CASC-J9 (2018) competition problems (FOFdivision). Experimental results show that C S E _ E 1.0 indeed enhances the performance of E to a certain extent.
Towards a top-down approach for an automatic discourse analysis for Basque: Segmentation and Central Unit detection tool

Lately, discourse structure has received considerable attention due to the benefits its application offers in several NLP tasks such as opinion mining, summarization, question answering, text simplification, among others. When automatically analyzing texts, discourse parsers typically perform two different tasks: i) identification of basic discourse units (text segmentation) ii) linking discourse units by means of discourse relations, building structures such as trees or graphs. The resulting discourse structures are, in general terms, accurate at intra-sentence discourse-level relations, however they fail to capture the correct inter-sentence relations. Detecting the main discourse unit (the Central Unit) is helpful for discourse analyzers (and also for manual annotation) in improving their results in rhetorical labeling. Bearing this in mind, we set out to build the first two steps of a discourse parser following a top-down strategy: i) to find discourse units, ii) to detect the Central Unit. The final step, i.e. assigning rhetorical relations, remains to be worked on in the immediate future. In accordance with this strategy, our paper presents a tool consisting of a discourse segmenter and an automatic Central Unit detector.
Case-Based Learning in Forensic Accounting Education

This study aims to examine the influence of the case based learning method application and motivation toward understanding of forensic accounting with emotional intelligence as moderator. The population of this research is the undergraduate students of accounting department who have taken forensic accounting course at Warmadewa University. Sampling technique in this research is purposive sampling with certain criteria such the student who have taken forensic accounting course. The research design is survey research design by distributing questionnaires to respondents. Data analysis uses a moderated regression analysis (MRA). The results of this research shows that the application of case-based learning method and motivation have a significant positive effect on forensic accounting comprehension, and it is proved that emotional intelligence is able to moderate the influence of application of case based learning method and motivation toward the understanding of forensic accounting
Does Inflation Rate Convergence Spur Exchange Rate Volatility? Empirical Evidence from Sub-Saharan Africa

Attaining a monetary union is an ambition for most regional economic blocks. However, the arrangement towards monetary union for the East African nations has remained indifferent. The inflation rate is critical for EAC members to achieve a level of harmonization required for establishing a stable and sustainable monetary union. Most existing studies on the relationship show conflicting results and mainly focus on developed countries. It was against this backdrop that the study sought to determine the effect of convergence in the inflation rate, a panel data of 5 countries, for the period 2000-2016 was used. Secondary data was obtained from the Statistical Abstracts and World Bank Report. The study was guided by the Optimum Currency Area framework. The study made use of Standard deviation and LCC to determine convergence and panel unit root respectively. LCC test established that the exchange rate and inflation are stationary at level. Standard deviation test concluded that inflation and exchange rate manifested a negative relationship. This means that convergence in inflation among the EAC countries reduces exchange rate variability within the region. Thus a policy should be made towards controlling this effect resulting from Inflation as East Africa bids for monetary union.