bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Heavy Metals on Physiology and Biochemical Profiling in Cyamopsis tetragonoloba L
Authors: Bharti Vivek , Jain Usha , Mohil Praveen
Published: August 2019

In the present investigation effect of abiotic stress in the form of heavy metals was studied for their effect on biochemical parameters including seedling growth and fresh weight of Cyamopsis tetragonoloba L. For this seeds treated with different concentrations (10, 50, 100, 200, 500 and 1000 ppm) of Cu, Cd, Pb, Ni and Zn along with control.  Maximum reduction in seed germination percentage was observed at 1000 ppm concentration. It decreased up to 40% in Cd, 60% in Pb 83% in Ni and Zn and 93% in Cu, whereas it was found to be 100% in the control. The shoot length at 500 ppm concentration was drastically reduced to led than one third of control in case of cadmium. At 500 ppm concentration of lead it was reduced to less than half of control. It was observed that heavy metal significantly reduced the fresh weight of seedling of Cyamopsis tetragonoloba. The percentage decreased in the fresh wright of seedling due to heavy metal application as compared to control. With increases in concentrations of heavy metals there was a gradual decrease in total chorophyll content in Cyamopsis tetragonoloba  except in respect of Zn and Cu up to 10 and 50 ppm concentration, respectively where it was superior to control. It was observed that Zn and Cu were less inhibitory to the amount of total carotenoid content in comparison to other heavy metals. In the control of carotenoid content was 0.37 mg/g fresh weight which decreased to 0.1 mg/g in Cd 0.20 mg/g in Pb, 0.23 mg/g in Ni 0.26 mg/g in Zn and Cu of fresh weight  at 1000 ppm concentration. All biochemical parameters reduced at 1000ppm increasing from 10 ppm.
Hasfali endometriosis: differenciáldiagnosztikai nehézségek a radiológus szemszögéből
Authors: Csorba Csenge , Pásztor Norbert , Szalma Emese , Kovács Gabriella , Palkó András , Fejes Zsuzsanna
Published: August 2019

Abstract:

The incidence of endometriosis, including atypical forms of the disease, has been continuously growing, thus increasingly challenging for the imaging specialists as well. We conducted a retrospective study to analyze the results of ultrasound-guided interventions between 2016 and 2018. All interventions were performed in female patients due to uncertain abdominal wall lesions at the University of Szeged, Hungary. The abdominal wall lesions were incidentally detected, one by CT, the others by ultrasound examinations. We identified 12 cases during the study period. The average age of the patients was 59 years (29–79), 8 of them had abdominal surgery in their medical history. The mean diameter of the masses was 34.4 mm (20–49 mm). Since the indication of imaging examinations was the evaluation of a known or suspected malignancy, four patients had undergone an MRI prior to the biopsy. In addition, ultrasound-guided biopsy was not performed in another two patients, and the diagnosis was established by histological examination of the surgically removed specimens. The histological examination revealed malignant primary serous epithelial tumor in one case, metastases in six cases, endometriosis in six patients and abdominal wall abscess was found in one patient. Endometriosis was more frequent in the younger patients. The likelihood of endometriosis as a cause of abdominal wall lesions of younger, premenopausal female patients is rather high, especially with obstetrical or gynaecological operations in the medical history. Ultrasound plays a primary role in the detection and therapy planning of these lesions. Orv Hetil. 2019; 160(35): 1395–1403.
A szellemi fogyatékosok fogászati-szájsebészeti ellátásának múltja, jelene és remélt jövője Magyarországon
Authors: Szmirnova Ilona , Gellérd Emese , Pintér Gábor Tamás , Szmirnov György , Németh Zsolt , Szabó György
Published: August 2019

Abstract:

Introduction: Dental care for mentally disabled people poses a growing challenge for healthcare. In Hungary, the number of mentally disabled people needing special dental care is ca. 100 000. Aim: The aim of our retrospective analysis is to provide a summary of the demographic data and the treatment outcomes of patients with mental disorders treated at the Department of the Oral and Maxillofacial Surgery of the Semmelweis University in the past five years. Method: Dental care for patients with a severe level of mental disability can be carried out in general anaesthesia only. At Semmelweis University, in the Oral and Maxillofacial Department, 1717 mentally disabled adults received dental care during the past five years. (Patients included people with a mild, medium or severe level of mental disability, patients with Down’s syndrome, autism, epilepsy or panic disorder.) Results: The single biggest achievement seems to be the fact that the issue of acute dental care and oral surgery has basically been settled. A workable relationship has been forged with foundations and organizations dealing with the problems of these patients. It has been realized, however, that in the case of mentally disabled patients there is an enormous need for prevention and ongoing care. Conclusions: Up to now no survey has been carried out in Hungary with the aim of objectively revealing the dental care needs of these patients. Internationally, however, several surveys have been published. It can be stated on the basis of these that both caries frequency and the presence of parodontal diseases increase in correlation with age and the level of disability. Oral hygiene is insufficient, patients or their caretakers do not get proper information, only a few of them receive adequate training and they are not motivated to keep up oral health. Dental care, except for tending acute cases, is not satisfactory. For the sake of prevention, cooperation is needed with non-governmental organizations, foundations, special education teachers and psychiatrists specialized in this field. Orv Hetil. 2019; 160(35): 1380–1386.
Responding responsibly
Authors: Hiss Florian
Published: August 2019

When responding to questions, speakers usually do not only report what they know about the question being asked. This study explores a series of survey responses on how respondents explicitly and implicitly manage responsibility for what they say. It builds on a telephone survey on workplace multilingualism in Northern Norway and focuses on one particular interview question that addresses practical as well as ideological concerns: the use of traditional minority languages at work. As representatives of regional companies, the informants face and handle responsibilities relating to their professional and organisational roles, the interactional relations of the structured interview, and the sociolinguistic and sociocultural surroundings. The choices they make when handling responsibility for their responses go beyond the realm of each of these taken separately. The study therefore highlights the importance of a perspective across different scopes and contexts. Asking what choices respondents make to respond in a responsible way involves not only the content of the question, but also implicit ideological concerns and interactional relations, as well as a range of professional and other social roles.
Patient participation, authority and understanding – A case study of communication patterns in a geriatric consultation
Authors: Hellström Muhli Ulla , Saferstein Barry , Siouta Eleni
Published: August 2019

Objectives: The objectives are to examine communication patterns in the discourse between a female geriatric patient and a pharmacist concerning use of an inhaler in order to understand the communicative components of consultation interaction that support the patient learning and demonstrate understanding of medical information.

Methods: This article is a qualitative case study from a project that video recorded 40 clinical consultations. The case was selected as a representative example of the use of material and visual information resources during a clinical consultation between a patient and a pharmacist in a geriatric setting.

Results: The patient's understanding of the clinician's medical advice did not result simply from repeating the clinician's demonstration. Rather, the patient's discussion and manipulation of the inhaler produced a communication pattern that increased her authority over topics and forms of expression.

Conclusion: A combination of verbal, visual and tactile information resources produced pattterns of interaction that enhanced the patient's participation, authority and understanding of medical information.

Practice implications: Many consultations can incorporate use of visual and material information resources to improve patients’ participation in treatment decisions.
The reception of US student motivation letters
Authors: Fernández-Gil María Jesús
Published: August 2019

Although student motivation letters are yet to receive interest within Spanish scholarly circles, the genre is starting to be used to gauge students’ potential for university admission, especially at the master's and doctoral levels. This paper intends to establish the current status of motivation letters in the Spanish university discourse community and to determine the genre's basic features (communicative purpose, structure, content and style) as it is developing in Spanish. Public information on Spanish motivation letters, drawn from a survey of online and print material for writing effective motivation letters and from a selection of 25 Master of Business Administration programmes, indicates that the genre is not well established. Genre analysis of eight samples specifically tailored by Spanish universities further points towards genre instability and differences – as regards salient features – from its counterpart in the United States. My findings show that the communicative purpose of the genre within the Spanish context is informed by the cultural norms and preferences shared by members of the Spanish academic milieu.
RETRACTED: Manufacturing and evaluation of elastic properties of glass fiber reinforced polymers material
Authors: Thien Nguyen Luong , The Nguyen Tien , Quyet Tran Nguyen
Published: August 2019

VJMech’s Editors have decided to retract this article because it duplicates significant parts of papers that had previously published in other journals. The authors have misconduct concerning plagiarism of papers without citation.We really apologies to readers of the journal that this was not detected during the submission and review process.
Understanding Persuasion Cascades in Online Product Rating Systems
Authors: Xie Hong , Li Yongkun , Lui John C.S.
Published: August 2019

Online product rating systems have become an indispensable component for numerous web services such as Amazon, eBay, Google play store and TripAdvisor. One functionality of such systems is to uncover the product quality via product ratings (or reviews) contributed by consumers. However, a well-known psychological phenomenon called “messagebased persuasion” lead to “biased” product ratings in a cascading manner (we call this the persuasion cascade). This paper investigates: (1) How does the persuasion cascade influence the product quality estimation accuracy? (2) Given a real-world product rating dataset, how to infer the persuasion cascade and analyze it to draw practical insights? We first develop a mathematical model to capture key factors of a persuasion cascade. We formulate a high-order Markov chain to characterize the opinion dynamics of a persuasion cascade and prove the convergence of opinions. We further bound the product quality estimation error for a class of rating aggregation rules including the averaging scoring rule, via the matrix perturbation theory and the Chernoff bound. We also design a maximum likelihood algorithm to infer parameters of the persuasion cascade. We conduct experiments on the data from Amazon and TripAdvisor, and show that persuasion cascades notably exist, but the average scoring rule has a small product quality estimation error under practical scenarios.
Hierarchical Classification Based on Label Distribution Learning
Authors: Xu Changdong , Geng Xin
Published: August 2019

Hierarchical classification is a challenging problem where the class labels are organized in a predefined hierarchy. One primary challenge in hierarchical classification is the small training set issue of the local module. The local classifiers in the previous hierarchical classification approaches are prone to over-fitting, which becomes a major bottleneck of hierarchical classification. Fortunately, the labels in the local module are correlated, and the siblings of the true label can provide additional supervision information for the instance. This paper proposes a novel method to deal with the small training set issue. The key idea of the method is to represent the correlation among the labels by the label distribution. It generates a label distribution that contains the supervision information of each label for the given instance, and then learns a mapping from the instance to the label distribution. Experimental results on several hierarchical classification datasets show that our method significantly outperforms other state-of-theart hierarchical classification approaches.
Unsupervised Fake News Detection on Social Media: A Generative Approach
Authors: Yang Shuo , Shu Kai , Wang Suhang , Gu Renjie , Wu Fan , Liu Huan
Published: August 2019

Social media has become one of the main channels for people to access and consume news, due to the rapidness and low cost of news dissemination on it. However, such properties of social media also make it a hotbed of fake news dissemination, bringing negative impacts on both individuals and society. Therefore, detecting fake news has become a crucial problem attracting tremendous research effort. Most existing methods of fake news detection are supervised, which require an extensive amount of time and labor to build a reliably annotated dataset. In search of an alternative, in this paper, we investigate if we could detect fake news in an unsupervised manner. We treat truths of news and users’ credibility as latent random variables, and exploit users’ engagements on social media to identify their opinions towards the authenticity of news. We leverage a Bayesian network model to capture the conditional dependencies among the truths of news, the users’ opinions, and the users’ credibility. To solve the inference problem, we propose an efficient collapsed Gibbs sampling approach to infer the truths of news and the users’ credibility without any labelled data. Experiment results on two datasets show that the proposed method significantly outperforms the compared unsupervised methods.
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction
Authors: Yao Huaxiu , Tang Xianfeng , Wei Hua , Zheng Guanjie , Li Zhenhui
Published: August 2019

Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For example, an accurate taxi demand prediction can assist taxi companies in pre-allocating taxis. The key challenge of traffic prediction lies in how to model the complex spatial dependencies and temporal dynamics. Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i.e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical. However, in practice the spatial dependence could be dynamic (i.e., changing from time to time), and the temporal dynamics could have some perturbation from one period to another period. In this paper, we make two important observations: (1) the spatial dependencies between locations are dynamic; and (2) the temporal dependency follows daily and weekly pattern but it is not strictly periodic for its dynamic temporal shifting. To address these two issues, we propose a novel Spatial-Temporal Dynamic Network (STDN), in which a flow gating mechanism is introduced to learn the dynamic similarity between locations, and a periodically shifted attention mechanism is designed to handle long-term periodic temporal shifting. To the best of our knowledge, this is the first work that tackle both issues in a unified framework. Our experimental results on real-world traffic datasets verify the effectiveness of the proposed method.
Iterative Classroom Teaching
Authors: Yeo Teresa , Kamalaruban Parameswaran , Singla Adish , Merchant Arpit , Asselborn Thibault , Faucon Louis , Dillenbourg Pierre , Cevher Volkan
Published: August 2019

We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rates. We prove that a teacher with full knowledge about the learning dynamics of the students can teach a target concept to the entire classroom using O(min{d,N}log 1/ɛ) exam-ples, where d is the ambient dimension of the problem, N is the number of learners, and ɛ is the accuracy parameter. We show the robustness of our teaching strategy when the teacher has limited knowledge of the learners’ internal dynamics as provided by a noisy oracle. Further, we study the trade-off between the learners’ workload and the teacher’s cost in teaching the target concept. Our experiments validate our theoretical results and suggest that appropriately partitioning the classroom into homogenous groups provides a balance between these two objectives.
Network Recasting: A Universal Method for Network Architecture Transformation
Authors: Yu Joonsang , Kang Sungbum , Choi Kiyoung
Published: August 2019

This paper proposes network recasting as a general method for network architecture transformation. The primary goal of this method is to accelerate the inference process through the transformation, but there can be many other practical applications. The method is based on block-wise recasting; it recasts each source block in a pre-trained teacher network to a target block in a student network. For the recasting, a target block is trained such that its output activation approximates that of the source block. Such a block-by-block recasting in a sequential manner transforms the network architecture while preserving the accuracy. This method can be used to transform an arbitrary teacher network type to an arbitrary student network type. It can even generate a mixed-architecture network that consists of two or more types of block. The network recasting can generate a network with fewer parameters and/or activations, which reduce the inference time significantly. Naturally, it can be used for network compression by recasting a trained network into a smaller network of the same type. Our experiments show that it outperforms previous compression approaches in terms of actual speedup on a GPU.
Multi-Order Attentive Ranking Model for Sequential Recommendation
Authors: Yu Lu , Zhang Chuxu , Liang Shangsong , Zhang Xiangliang
Published: August 2019

In modern e-commerce, the temporal order behind users’ transactions implies the importance of exploiting the transition dependency among items for better inferring what a user prefers to interact in “near future”. The types of interaction among items are usually divided into individual-level interaction that can stand out the transition order between a pair of items, or union-level relation between a set of items and single one. However, most of existing work only captures one of them from a single view, especially on modeling the individual-level interaction. In this paper, we propose a Multi-order Attentive Ranking Model (MARank) to unify both individual- and union-level item interaction into preference inference model from multiple views. The idea is to represent user’s short-term preference by embedding user himself and a set of present items into multi-order features from intermedia hidden status of a deep neural network. With the help of attention mechanism, we can obtain a unified embedding to keep the individual-level interactions with a linear combination of mapped items’ features. Then, we feed the aggregated embedding to a designed residual neural network to capture union-level interaction. Thorough experiments are conducted to show the features of MARank under various component settings. Furthermore experimental results on several public datasets show that MARank significantly outperforms the state-of-the-art baselines on different evaluation metrics. The source code can be found at https://github.com/voladorlu/MARank.
Interpreting Deep Models for Text Analysis via Optimization and Regularization Methods
Authors: Yuan Hao , Chen Yongjun , Hu Xia , Ji Shuiwang
Published: August 2019

Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing (NLP) tasks. However, most existing approaches only focus on improving the performance of models but ignore their interpretability. In this work, we propose an approach to investigate the meaning of hidden neurons of the convolutional neural network (CNN) models. We first employ saliency map and optimization techniques to approximate the detected information of hidden neurons from input sentences. Then we develop regularization terms and explore words in vocabulary to interpret such detected information. Experimental results demonstrate that our approach can identify meaningful and reasonable interpretations for hidden spatial locations. Additionally, we show that our approach can describe the decision procedure of deep NLP models.
Partially Observable Multi-Sensor Sequential Change Detection: A Combinatorial Multi-Armed Bandit Approach
Authors: Zhang Chen , Hoi Steven C.H.
Published: August 2019

This paper explores machine learning to address a problem of Partially Observable Multi-sensor Sequential Change Detection (POMSCD), where only a subset of sensors can be observed to monitor a target system for change-point detection at each online learning round. In contrast to traditional Multisensor Sequential Change Detection tasks where all the sensors are observable, POMSCD is much more challenging because the learner not only needs to detect on-the-fly whether a change occurs based on partially observed multi-sensor data streams, but also needs to cleverly choose a subset of informative sensors to be observed in the next learning round, in order to maximize the overall sequential change detection performance. In this paper, we present the first online learning study to tackle POMSCD in a systemic and rigorous way. Our approach has twofold novelties: (i) we attempt to detect changepoints from partial observations effectively by exploiting potential correlations between sensors, and (ii) we formulate the sensor subset selection task as a Multi-Armed Bandit (MAB) problem and develop an effective adaptive sampling strategy using MAB algorithms. We offer theoretical analysis for the proposed online learning solution, and further validate its empirical performance via an extensive set of numerical studies together with a case study on real-world data sets.
One for All: Neural Joint Modeling of Entities and Events
Authors: Nguyen Trung Minh , Nguyen Thien Huu
Published: August 2019

The previous work for event extraction has mainly focused on the predictions for event triggers and argument roles, treating entity mentions as being provided by human annotators. This is unrealistic as entity mentions are usually predicted by some existing toolkits whose errors might be propagated to the event trigger and argument role recognition. Few of the recent work has addressed this problem by jointly predicting entity mentions, event triggers and arguments. However, such work is limited to using discrete engineering features to represent contextual information for the individual tasks and their interactions. In this work, we propose a novel model to jointly perform predictions for entity mentions, event triggers and arguments based on the shared hidden representations from deep learning. The experiments demonstrate the benefits of the proposed method, leading to the state-of-the-art performance for event extraction.
Analyzing Compositionality-Sensitivity of NLI Models
Authors: Nie Yixin , Wang Yicheng , Bansal Mohit
Published: August 2019

Success in natural language inference (NLI) should require a model to understand both lexical and compositional semantics. However, through adversarial evaluation, we find that several state-of-the-art models with diverse architectures are over-relying on the former and fail to use the latter. Further, this compositionality unawareness is not reflected via standard evaluation on current datasets. We show that removing RNNs in existing models or shuffling input words during training does not induce large performance loss despite the explicit removal of compositional information. Therefore, we propose a compositionality-sensitivity testing setup that analyzes models on natural examples from existing datasets that cannot be solved via lexical features alone (i.e., on which a bag-of-words model gives a high probability to one wrong label), hence revealing the models’ actual compositionality awareness. We show that this setup not only highlights the limited compositional ability of current NLI models, but also differentiates model performance based on design, e.g., separating shallow bag-of-words models from deeper, linguistically-grounded tree-based models. Our evaluation setup is an important analysis tool: complementing currently existing adversarial and linguistically driven diagnostic evaluations, and exposing opportunities for future work on evaluating models’ compositional understanding.
Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces
Authors: Prokhorov Victor , Pilehvar Mohammad Taher , Kartsaklis Dimitri , Lio Pietro , Collier Nigel
Published: August 2019

Word embedding techniques heavily rely on the abundance of training data for individual words. Given the Zipfian distribution of words in natural language texts, a large number of words do not usually appear frequently or at all in the training data. In this paper we put forward a technique that exploits the knowledge encoded in lexical resources, such as WordNet, to induce embeddings for unseen words. Our approach adapts graph embedding and cross-lingual vector space transformation techniques in order to merge lexical knowledge encoded in ontologies with that derived from corpus statistics. We show that the approach can provide consistent performance improvements across multiple evaluation benchmarks: in-vitro, on multiple rare word similarity datasets, and invivo, in two downstream text classification tasks.
Data-to-Text Generation with Content Selection and Planning
Authors: Puduppully Ratish , Dong Li , Lapata Mirella
Published: August 2019

Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order. In this work, we present a neural network architecture which incorporates content selection and planning without sacrificing end-to-end training. We decompose the generation task into two stages. Given a corpus of data records (paired with descriptive documents), we first generate a content plan highlighting which information should be mentioned and in which order and then generate the document while taking the content plan into account. Automatic and human-based evaluation experiments show that our model1 outperforms strong baselines improving the state-of-the-art on the recently released RotoWIRE dataset.
COALA: A Neural Coverage-Based Approach for Long Answer Selection with Small Data
Authors: Rücklé Andreas , Moosavi Nafise Sadat , Gurevych Iryna
Published: August 2019

Current neural network based community question answering (cQA) systems fall short of (1) properly handling long answers which are common in cQA; (2) performing under small data conditions, where a large amount of training data is unavailable—i.e., for some domains in English and even more so for a huge number of datasets in other languages; and (3) benefiting from syntactic information in the model—e.g., to differentiate between identical lexemes with different syntactic roles. In this paper, we propose COALA, an answer selection approach that (a) selects appropriate long answers due to an effective comparison of all question-answer aspects, (b) has the ability to generalize from a small number of training examples, and (c) makes use of the information about syntactic roles of words. We show that our approach outperforms existing answer selection models by a large margin on six cQA datasets from different domains. Furthermore, we report the best results on the passage retrieval benchmark WikiPassageQA.
Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function
Authors: Sachan Devendra Singh , Zaheer Manzil , Salakhutdinov Ruslan
Published: August 2019

In this paper, we study bidirectional LSTM network for the task of text classification using both supervised and semisupervised approaches. Several prior works have suggested that either complex pretraining schemes using unsupervised methods such as language modeling (Dai and Le 2015; Miyato, Dai, and Goodfellow 2016) or complicated models (Johnson and Zhang 2017) are necessary to achieve a high classification accuracy. However, we develop a training strategy that allows even a simple BiLSTM model, when trained with cross-entropy loss, to achieve competitive results compared with more complex approaches. Furthermore, in addition to cross-entropy loss, by using a combination of entropy minimization, adversarial, and virtual adversarial losses for both labeled and unlabeled data, we report state-of-theart results for text classification task on several benchmark datasets. In particular, on the ACL-IMDB sentiment analysis and AG-News topic classification datasets, our method outperforms current approaches by a substantial margin. We also show the generality of the mixed objective function by improving the performance on relation extraction task.1
A Hierarchical Multi-Task Approach for Learning Embeddings from Semantic Tasks
Authors: Sanh Victor , Wolf Thomas , Ruder Sebastian
Published: August 2019

Much effort has been devoted to evaluate whether multi-task learning can be leveraged to learn rich representations that can be used in various Natural Language Processing (NLP) down-stream applications. However, there is still a lack of understanding of the settings in which multi-task learning has a significant effect. In this work, we introduce a hierarchical model trained in a multi-task learning setup on a set of carefully selected semantic tasks. The model is trained in a hierarchical fashion to introduce an inductive bias by supervising a set of low level tasks at the bottom layers of the model and more complex tasks at the top layers of the model. This model achieves state-of-the-art results on a number of tasks, namely Named Entity Recognition, Entity Mention Detection and Relation Extraction without hand-engineered features or external NLP tools like syntactic parsers. The hierarchical training supervision induces a set of shared semantic representations at lower layers of the model. We show that as we move from the bottom to the top layers of the model, the hidden states of the layers tend to represent more complex semantic information.
Learning to Embed Sentences Using Attentive Recursive Trees
Authors: Shi Jiaxin , Hou Lei , Li Juanzi , Liu Zhiyuan , Zhang Hanwang
Published: August 2019

Sentence embedding is an effective feature representation for most deep learning-based NLP tasks. One prevailing line of methods is using recursive latent tree-structured networks to embed sentences with task-specific structures. However, existing models have no explicit mechanism to emphasize taskinformative words in the tree structure. To this end, we propose an Attentive Recursive Tree model (AR-Tree), where the words are dynamically located according to their importance in the task. Specifically, we construct the latent tree for a sentence in a proposed important-first strategy, and place more attentive words nearer to the root; thus, AR-Tree can inherently emphasize important words during the bottomup composition of the sentence embedding. We propose an end-to-end reinforced training strategy for AR-Tree, which is demonstrated to consistently outperform, or be at least comparable to, the state-of-the-art sentence embedding methods on three sentence understanding tasks.
A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues
Authors: Shi Zhouxing , Huang Minlie
Published: August 2019

Discourse structures are beneficial for various NLP tasks such as dialogue understanding, question answering, sentiment analysis, and so on. This paper presents a deep sequential model for parsing discourse dependency structures of multi-party dialogues. The proposed model aims to construct a discourse dependency tree by predicting dependency relations and constructing the discourse structure jointly and alternately. It makes a sequential scan of the Elementary DiscourseUnits(EDUs)1 in a dialogue. For each EDU, the model decides to which previous EDU the current one should link and what the corresponding relation type is. The predicted link and relation type are then used to build the discourse structure incrementally with a structured encoder. During link prediction and relation classification, the model utilizes not only local information that represents the concerned EDUs, but also global information that encodes the EDU sequence and the discourse structure that is already built at the current step. Experiments show that the proposed model outperforms all the state-of-the-art baselines.
GlobalTrait: Personality Alignment of Multilingual Word Embeddings
Authors: Siddique Farhad Bin , Bertero Dario , Fung Pascale
Published: August 2019

We propose a multilingual model to recognize Big Five Personality traits from text data in four different languages: English, Spanish, Dutch and Italian. Our analysis shows that words having a similar semantic meaning in different languages do not necessarily correspond to the same personality traits. Therefore, we propose a personality alignment method, GlobalTrait, which has a mapping for each trait from the source language to the target language (English), such that words that correlate positively to each trait are close together in the multilingual vector space. Using these aligned embeddings for training, we can transfer personality related training features from high-resource languages such as English to other low-resource languages, and get better multilingual results, when compared to using simple monolingual and unaligned multilingual embeddings. We achieve an average F-score increase (across all three languages except English) from 65 to 73.4 (+8.4), when comparing our monolingual model to multilingual using CNN with personality aligned embeddings. We also show relatively good performance in the regression tasks, and better classification results when evaluating our model on a separate Chinese dataset.
Exploring Knowledge Graphs in an Interpretable Composite Approach for Text Entailment
Authors: Silva Vivian S. , Freitas André , Handschuh Siegfried
Published: August 2019

Recognizing textual entailment is a key task for many semantic applications, such as Question Answering, Text Summarization, and Information Extraction, among others. Entailment scenarios can range from a simple syntactic variation to more complex semantic relationships between pieces of text, but most approaches try a one-size-fits-all solution that usually favors some scenario to the detriment of another. We propose a composite approach for recognizing text entailment which analyzes the entailment pair to decide whether it must be resolved syntactically or semantically. We also make the answer interpretable: whenever an entailment is solved semantically, we explore a knowledge base composed of structured lexical definitions to generate natural language humanlike justifications, explaining the semantic relationship holding between the pieces of text. Besides outperforming wellestablished entailment algorithms, our composite approach gives an important step towards Explainable AI, using world knowledge to make the semantic reasoning process explicit and understandable.
Fast PMI-Based Word Embedding with Efficient Use of Unobserved Patterns
Authors: Soleimani Behrouz Haji , Matwin Stan
Published: August 2019

Continuous word representations that can capture the semantic information in the corpus are the building blocks of many natural language processing tasks. Pre-trained word embeddings are being used for sentiment analysis, text classification, question answering and so on. In this paper, we propose a new word embedding algorithm that works on a smoothed Positive Pointwise Mutual Information (PPMI) matrix which is obtained from the word-word co-occurrence counts. One of our major contributions is to propose an objective function and an optimization framework that exploits the full capacity of “negative examples”, the unobserved or insignificant wordword co-occurrences, in order to push unrelated words away from each other which improves the distribution of words in the latent space. We also propose a kernel similarity measure for the latent space that can effectively calculate the similarities in high dimensions. Moreover, we propose an approximate alternative to our algorithm using a modified Vantage Point tree and reduce the computational complexity of the algorithm to |V |log|V | with respect to the number of words in the vocabulary. We have trained various word embedding algorithms on articles of Wikipedia with 2.1 billion tokens and show that our method outperforms the state-of-the-art in most word similarity tasks by a good margin.
Distantly Supervised Entity Relation Extraction with Adapted Manual Annotations
Authors: Sun Changzhi , Wu Yuanbin
Published: August 2019

We investigate the task of distantly supervised joint entity relation extraction. It’s known that training with distant supervision will suffer from noisy samples. To tackle the problem, we propose to adapt a small manually labelled dataset to the large automatically generated dataset. By developing a novel adaptation algorithm, we are able to transfer the high quality but heterogeneous entity relation annotations in a robust and consistent way. Experiments on the benchmark NYT dataset show that our approach significantly outperforms state-ofthe-art methods.
QUAREL: A Dataset and Models for Answering Questions about Qualitative Relationships
Authors: Tafjord Oyvind , Clark Peter , Gardner Matt , Yih Wen-tau , Sabharwal Ashish
Published: August 2019

Many natural la guage questions require recognizing and reasoning with qualitative relationships (e.g., in science, economics, and medicine), but are challenging to answer with corpus-based methods. Qualitative modeling provides tools that support such reasoning, but the semantic parsing task of mapping questions into those models has formidable challenges. We present QUAREL, a dataset of diverse story questions involving qualitative relationships that characterize these challenges, and techniques that begin to address them. The dataset has 2771 questions relating 19 different types of quantities. For example, “Jenny observes that the robot vacuum cleaner moves slower on the living room carpet than on the bedroom carpet. Which carpet has more friction?” We contribute (1) a simple and flexible conceptual framework for representing these kinds of questions; (2) the QUAREL dataset, including logical forms, exemplifying the parsing challenges; and (3) two novel models for this task, built as extensions of type-constrained semantic parsing. The first of these models (called QUASP+) significantly outperforms off-the-shelf tools on QUAREL. The second (QUASP+ZERO) demonstrates zero-shot capability, i.e., the ability to handle new qualitative relationships without requiring additional training data, something not possible with previous models. This work thus makes inroads into answering complex, qualitative questions that require reasoning, and scaling to new relationships at low cost. The dataset and models are available at http://data.allenai.org/quarel.
Jointly Extracting Multiple Triplets with Multilayer Translation Constraints
Authors: Tan Zhen , Zhao Xiang , Wang Wei , Xiao Weidong
Published: August 2019

Triplets extraction is an essential and pivotal step in automatic knowledge base construction, which captures structural information from unstructured text corpus. Conventional extraction models use a pipeline of named entity recognition and relation classification to extract entities and relations, respectively, which ignore the connection between the two tasks. Recently, several neural network-based models were proposed to tackle the problem, and achieved state-of-the-art performance. However, most of them are unable to extract multiple triplets from a single sentence, which are yet commonly seen in real-life scenarios. To close the gap, we propose in this paper a joint neural extraction model for multitriplets, namely, TME, which is capable of adaptively discovering multiple triplets simultaneously in a sentence via ranking with translation mechanism. In experiment, TME exhibits superior performance and achieves an improvement of 37.6% on F1 score over state-of-the-art competitors.
Multi-Matching Network for Multiple Choice Reading Comprehension
Authors: Tang Min , Cai Jiaran , Zhuo Hankz Hankui
Published: August 2019

Multiple-choice machine reading comprehension is an important and challenging task where the machine is required to select the correct answer from a set of candidate answers given passage and question. Existing approaches either match extracted evidence with candidate answers shallowly or model passage, question and candidate answers with a single paradigm of matching. In this paper, we propose Multi-Matching Network (MMN) which models the semantic relationship among passage, question and candidate answers from multiple different paradigms of matching. In our MMN model, each paradigm is inspired by how human think and designed under a unified compose-match framework. To demonstrate the effectiveness of our model, we evaluate MMN on a large-scale multiple choice machine reading comprehension dataset (i.e. RACE). Empirical results show that our proposed model achieves a significant improvement compared to strong baselines and obtains state-of-the-art results.
Generating Live Soccer-Match Commentary from Play Data
Authors: Taniguchi Yasufumi , Feng Yukun , Takamura Hiroya , Okumura Manabu
Published: August 2019

We address the task of generating live soccer-match commentaries from play event data. This task has characteristics that (i) each commentary is only partially aligned with events, (ii) play event data contains many types of categorical and numerical attributes, (iii) live commentaries often mention player names and team names. For these reasons, we propose an encoder for play event data, which is enhanced with a gate mechanism. We also introduce an attention mechanism on events. In addition, we introduced placeholders and their reconstruction mechanism to enable the model to copy appropriate player names and team names from the input data. We conduct experiments on the play data of the English Premier League, provide a discussion on the result including generated commentaries.
Near-Lossless Binarization of Word Embeddings
Authors: Tissier Julien , Gravier Christophe , Habrard Amaury
Published: August 2019

Word embeddings are commonly used as a starting point in many NLP models to achieve state-of-the-art performances. However, with a large vocabulary and many dimensions, these floating-point representations are expensive both in terms of memory and calculations which makes them unsuitable for use on low-resource devices. The method proposed in this paper transforms real-valued embeddings into binary embeddings while preserving semantic information, requiring only 128 or 256 bits for each vector. This leads to a small memory footprint and fast vector operations. The model is based on an autoencoder architecture, which also allows to reconstruct original vectors from the binary ones. Experimental results on semantic similarity, text classification and sentiment analysis tasks show that the binarization of word embeddings only leads to a loss of ∼2% in accuracy while vector size is reduced by 97%. Furthermore, a top-k benchmark demonstrates that using these binary vectors is 30 times faster than using real-valued vectors.
Improving Hypernymy Prediction via Taxonomy Enhanced Adversarial Learning
Authors: Wang Chengyu , He Xiaofeng , Zhou Aoying
Published: August 2019

Hypernymy is a basic semantic relation in computational linguistics that expresses the “is-a” relation between a generic concept and its specific instances, serving as the backbone in taxonomies and ontologies. Although several NLP tasks related to hypernymy prediction have been extensively addressed, few methods have fully exploited the large number of hypernymy relations in Web-scale taxonomies.In this paper, we introduce the Taxonomy Enhanced Adversarial Learning (TEAL) for hypernymy prediction. We first propose an unsupervised measure U-TEAL to distinguish hypernymy with other semantic relations. It is implemented based on a word embedding projection network distantly trained over a taxonomy. To address supervised hypernymy detection tasks, the supervised model S-TEAL and its improved version, the adversarial supervised model AS-TEAL, are further presented. Specifically, AS-TEAL employs a coupled adversarial training algorithm to transfer hierarchical knowledge in taxonomies to hypernymy prediction models. We conduct extensive experiments to confirm the effectiveness of TEAL over three standard NLP tasks: unsupervised hypernymy classification, supervised hypernymy detection and graded lexical entailment. We also show that TEAL can be applied to non-English languages and can detect missing hypernymy relations in taxonomies.
What if We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks
Authors: Wang Haohan , Sun Da , Xing Eric P.
Published: August 2019

Nature language inference (NLI) task is a predictive task of determining the inference relationship of a pair of natural language sentences. With the increasing popularity of NLI, many state-of-the-art predictive models have been proposed with impressive performances. However, several works have noticed the statistical irregularities in the collected NLI data set that may result in an over-estimated performance of these models and proposed remedies. In this paper, we further investigate the statistical irregularities, what we refer as confounding factors, of the NLI data sets. With the belief that some NLI labels should preserve under swapping operations, we propose a simple yet effective way (swapping the two text fragments) of evaluating the NLI predictive models that naturally mitigate the observed problems. Further, we continue to train the predictive models with our swapping manner and propose to use the deviation of the model’s evaluation performances under different percentages of training text fragments to be swapped to describe the robustness of a predictive model. Our evaluation metrics leads to some interesting understandings of recent published NLI methods. Finally, we also apply the swapping operation on NLI models to see the effectiveness of this straightforward method in mitigating the confounding factor problems in training generic sentence embeddings for other NLP transfer tasks.