bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering
Authors: Deng Yang , Xie Yuexiang , Li Yaliang , Yang Min , Du Nan , Fan Wei , Lei Kai , Shen Ying
Published: August 2019

Answer selection and knowledge base question answering (KBQA) are two important tasks of question answering (QA) systems. Existing methods solve these two tasks separately, which requires large number of repetitive work and neglects the rich correlation information between tasks. In this paper, we tackle answer selection and KBQA tasks simultaneously via multi-task learning (MTL), motivated by the following motivations. First, both answer selection and KBQA can be regarded as a ranking problem, with one at text-level while the other at knowledge-level. Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), while KBQA can be improved by learning contextual information from answer selection. To fulfill the goal of jointly learning these two tasks, we propose a novel multi-task learning scheme that utilizes multi-view attention learned from various perspectives to enable these tasks to interact with each other as well as learn more comprehensive sentence representations. The experiments conducted on several real-world datasets demonstrate the effectiveness of the proposed method, and the performance of answer selection and KBQA is improved. Also, the multi-view attention scheme is proved to be effective in assembling attentive information from different representational perspectives.
Training Temporal Word Embeddings with a Compass
Authors: Di Carlo Valerio , Bianchi Federico , Palmonari Matteo
Published: August 2019

Temporal word embeddings have been proposed to support the analysis of word meaning shifts during time and to study the evolution of languages. Different approaches have been proposed to generate vector representations of words that embed their meaning during a specific time interval. However, the training process used in these approaches is complex, may be inefficient or it may require large text corpora. As a consequence, these approaches may be difficult to apply in resource-scarce domains or by scientists with limited in-depth knowledge of embedding models. In this paper, we propose a new heuristic to train temporal word embeddings based on the Word2vec model. The heuristic consists in using atemporal vectors as a reference, i.e., as a compass, when training the representations specific to a given time interval. The use of the compass simplifies the training process and makes it more efficient. Experiments conducted using state-of-the-art datasets and methodologies suggest that our approach outperforms or equals comparable approaches while being more robust in terms of the required corpus size.
A Pattern-Based Approach to Recognizing Time Expressions
Authors: Ding Wentao , Gao Guanji , Shi Linfeng , Qu Yuzhong
Published: August 2019

Recognizing time expressions is a fundamental and important task in many applications of natural language understanding, such as reading comprehension and question answering. Several newest state-of-the-art approaches have achieved good performance on recognizing time expressions. These approaches are black-boxed or based on heuristic rules, which leads to the difficulty in understanding the temporal information. On the contrary, classic rule-based or semantic parsing approaches can capture rich structural information, but their performances on recognition are not so good. In this paper, we propose a pattern-based approach, called PTime, which automatically generates and selects patterns for recognizing time expressions. In this approach, time expressions in training text are abstracted into type sequences by using fine-grained token types, thus the problem is transformed to select an appropriate subset of the sequential patterns. We use the Extended Budgeted Maximum Coverage (EBMC) model to optimize the pattern selection. The main idea is to maximize the correct token sequences matched by the selected patterns while the number of the mistakes should be limited by an adjustable budget. The interpretability of patterns and the adjustability of permitted number of mistakes make PTime a very promising approach for many applications. Experimental results show that PTime achieves a very competitive performance as compared with existing state-of-the-art approaches.
MNCN: A Multilingual Ngram-Based Convolutional Network for Aspect Category Detection in Online Reviews
Authors: Ghadery Erfan , Movahedi Sajad , Faili Heshaam , Shakery Azadeh
Published: August 2019

The advent of the Internet has caused a significant growth in the number of opinions expressed about products or services on e-commerce websites. Aspect category detection, which is one of the challenging subtasks of aspect-based sentiment analysis, deals with categorizing a given review sentence into a set of predefined categories. Most of the research efforts in this field are devoted to English language reviews, while there are a large number of reviews in other languages that are left unexplored. In this paper, we propose a multilingual method to perform aspect category detection on reviews in different languages, which makes use of a deep convolutional neural network with multilingual word embeddings. To the best of our knowledge, our method is the first attempt at performing aspect category detection on multiple languages simultaneously. Empirical results on the multilingual dataset provided by SemEval workshop demonstrate the effectiveness of the proposed method1.
Weakly-Supervised Hierarchical Text Classification
Authors: Meng Yu , Shen Jiaming , Zhang Chao , Han Jiawei
Published: August 2019

Hierarchical text classification, which aims to classify text documents into a given hierarchy, is an important task in many real-world applications. Recently, deep neural models are gaining increasing popularity for text classification due to their expressive power and minimum requirement for feature engineering. However, applying deep neural networks for hierarchical text classification remains challenging, because they heavily rely on a large amount of training data and meanwhile cannot easily determine appropriate levels of documents in the hierarchical setting. In this paper, we propose a weakly-supervised neural method for hierarchical text classification. Our method does not require a large amount of training data but requires only easy-to-provide weak supervision signals such as a few class-related documents or keywords. Our method effectively leverages such weak supervision signals to generate pseudo documents for model pre-training, and then performs self-training on real unlabeled data to iteratively refine the model. During the training process, our model features a hierarchical neural structure, which mimics the given hierarchy and is capable of determining the proper levels for documents with a blocking mechanism. Experiments on three datasets from different domains demonstrate the efficacy of our method compared with a comprehensive set of baselines.
CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling
Authors: Miao Ning , Zhou Hao , Mou Lili , Yan Rui , Li Lei
Published: August 2019

In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements. Existing language generation techniques are usually based on recurrent neural networks (RNNs). However, it is non-trivial to impose constraints on RNNs while maintaining generation quality, since RNNs generate sentences sequentially (or with beam search) from the first word to the last. In this paper, we propose CGMH, a novel approach using Metropolis-Hastings sampling for constrained sentence generation. CGMH allows complicated constraints such as the occurrence of multiple keywords in the target sentences, which cannot be handled in traditional RNN-based approaches. Moreover, CGMH works in the inference stage, and does not require parallel corpora for training. We evaluate our method on a variety of tasks, including keywords-to-sentence generation, unsupervised sentence paraphrasing, and unsupervised sentence error correction. CGMH achieves high performance compared with previous supervised methods for sentence generation. Our code is released at https://github.com/NingMiao/CGMH
Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model
Authors: Mielke Sebastian J. , Eisner Jason
Published: August 2019

We show how the spellings of known words can help us deal with unknown words in open-vocabulary NLP tasks. The method we propose can be used to extend any closedvocabulary generative model, but in this paper we specifically consider the case of neural language modeling. Our Bayesian generative story combines a standard RNN language model (generating the word tokens in each sentence) with an RNNbased spelling model (generating the letters in each word type). These two RNNs respectively capture sentence structure and word structure, and are kept separate as in linguistics. By invoking the second RNN to generate spellings for novel words in context, we obtain an open-vocabulary language model. For known words, embeddings are naturally inferred by combining evidence from type spelling and token context. Comparing to baselines (including a novel strong baseline), we beat previous work and establish state-of-the-art results on multiple datasets.
Combining Fact Extraction and Verification with Neural Semantic Matching Networks
Authors: Nie Yixin , Chen Haonan , Bansal Mohit
Published: August 2019

The increasing concern with misinformation has stimulated research efforts on automatic fact checking. The recentlyreleased FEVER dataset introduced a benchmark factverification task in which a system is asked to verify a claim using evidential sentences from Wikipedia documents. In this paper, we present a connected system consisting of three homogeneous neural semantic matching models that conduct document retrieval, sentence selection, and claim verification jointly for fact extraction and verification. For evidence retrieval (document retrieval and sentence selection), unlike traditional vector space IR models in which queries and sources are matched in some pre-designed term vector space, we develop neural models to perform deep semantic matching from raw textual input, assuming no intermediate term representation and no access to structured external knowledge bases. We also show that Pageview frequency can also help improve the performance of evidence retrieval results, that later can be matched by using our neural semantic matching network. For claim verification, unlike previous approaches that simply feed upstream retrieved evidence and the claim to a natural language inference (NLI) model, we further enhance the NLI model by providing it with internal semantic relatedness scores (hence integrating it with the evidence retrieval modules) and ontological WordNet features. Experiments on the FEVER dataset indicate that (1) our neural semantic matching method outperforms popular TF-IDF and encoder models, by significant margins on all evidence retrieval metrics, (2) the additional relatedness score and WordNet features improve the NLI model via better semantic awareness, and (3) by formalizing all three subtasks as a similar semantic matching problem and improving on all three stages, the complete model is able to achieve the state-of-the-art results on the FEVER test set (two times greater than baseline results).1
HAS-QA: Hierarchical Answer Spans Model for Open-Domain Question Answering
Authors: Pang Liang , Lan Yanyan , Guo Jiafeng , Xu Jun , Su Lixin , Cheng Xueqi
Published: August 2019

This paper is concerned with open-domain question answering (i.e., OpenQA). Recently, some works have viewed this problem as a reading comprehension (RC) task, and directly applied successful RC models to it. However, the performances of such models are not so good as that in the RC task. In our opinion, the perspective of RC ignores three characteristics in OpenQA task: 1) many paragraphs without the answer span are included in the data collection; 2) multiple answer spans may exist within one given paragraph; 3) the end position of an answer span is dependent with the start position. In this paper, we first propose a new probabilistic formulation of OpenQA, based on a three-level hierarchical structure, i.e., the question level, the paragraph level and the answer span level. Then a Hierarchical Answer Spans Model (HASQA) is designed to capture each probability. HAS-QA has the ability to tackle the above three problems, and experiments on public OpenQA datasets show that it significantly outperforms traditional RC baselines and recent OpenQA baselines.
Paraphrase Diversification Using Counterfactual Debiasing
Authors: Park Sunghyun , Hwang Seung-won , Chen Fuxiang , Choo Jaegul , Ha Jung-Woo , Kim Sunghun , Yim Jinyeong
Published: August 2019

The problem of generating a set of diverse paraphrase sentences while (1) not compromising the original meaning of the original sentence, and (2) imposing diversity in various semantic aspects, such as a lexical or syntactic structure, is examined. Existing work on paraphrase generation has focused more on the former, and the latter was trained as a fixed style transfer, such as transferring from positive to negative sentiments, even at the cost of losing semantics. In this work, we consider style transfer as a means of imposing diversity, with a paraphrasing correctness constraint that the target sentence must remain a paraphrase of the original sentence. However, our goal is to maximize the diversity for a set of k generated paraphrases, denoted as the diversified paraphrase (DP) problem. Our key contribution is deciding the style guidance at generation towards the direction of increasing the diversity of output with respect to those generated previously. As pre-materializing training data for all style decisions is impractical, we train with biased data, but with debiasing guidance. Compared to state-of-the-art methods, our proposed model can generate more diverse and yet semantically consistent paraphrase sentences. That is, our model, trained with the MSCOCO dataset, achieves the highest embedding scores, .94/.95/.86, similar to state-of-the-art results, but with a lower mBLEU score (more diverse) by 8.73%.
Found in Translation: Learning Robust Joint Representations by Cyclic Translations between Modalities
Authors: Pham Hai , Liang Paul Pu , Manzini Thomas , Morency Louis-Philippe , Póczos Barnabás
Published: August 2019

Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities. The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities. However, existing work learns joint representations by requiring all modalities as input and as a result, the learned representations may be sensitive to noisy or missing modalities at test time. With the recent success of sequence to sequence (Seq2Seq) models in machine translation, there is an opportunity to explore new ways of learning joint representations that may not require all input modalities at test time. In this paper, we propose a method to learn robust joint representations by translating between modalities. Our method is based on the key insight that translation from a source to a target modality provides a method of learning joint representations using only the source modality as input. We augment modality translations with a cycle consistency loss to ensure that our joint representations retain maximal information from all modalities. Once our translation model is trained with paired multimodal data, we only need data from the source modality at test time for final sentiment prediction. This ensures that our model remains robust from perturbations or missing information in the other modalities. We train our model with a coupled translationprediction objective and it achieves new state-of-the-art results on multimodal sentiment analysis datasets: CMU-MOSI, ICTMMMO, and YouTube. Additional experiments show that our model learns increasingly discriminative joint representations with more input modalities while maintaining robustness to missing or perturbed modalities.
Jointly Learning to Label Sentences and Tokens
Authors: Rei Marek , Søgaard Anders
Published: August 2019

Learning to construct text representations in end-to-end systems can be difficult, as natural languages are highly compositional and task-specific annotated datasets are often limited in size. Methods for directly supervising language composition can allow us to guide the models based on existing knowledge, regularizing them towards more robust and interpretable representations. In this paper, we investigate how objectives at different granularities can be used to learn better language representations and we propose an architecture for jointly learning to label sentences and tokens. The predictions at each level are combined together using an attention mechanism, with token-level labels also acting as explicit supervision for composing sentence-level representations. Our experiments show that by learning to perform these tasks jointly on multiple levels, the model achieves substantial improvements for both sentence classification and sequence labeling.
Zero-Shot Neural Transfer for Cross-Lingual Entity Linking
Authors: Rijhwani Shruti , Xie Jiateng , Neubig Graham , Carbonell Jaime
Published: August 2019

Cross-lingual entity linking maps an entity mention in a source language to its corresponding entry in a structured knowledge base that is in a different (target) language. While previous work relies heavily on bilingual lexical resources to bridge the gap between the source and the target languages, these resources are scarce or unavailable for many low-resource languages. To address this problem, we investigate zero-shot cross-lingual entity linking, in which we assume no bilingual lexical resources are available in the source low-resource language. Specifically, we propose pivot-basedentity linking, which leverages information from a highresource “pivot” language to train character-level neural entity linking models that are transferred to the source lowresource language in a zero-shot manner. With experiments on 9 low-resource languages and transfer through a total of54 languages, we show that our proposed pivot-based framework improves entity linking accuracy 17% (absolute) on average over the baseline systems, for the zero-shot scenario.1 Further, we also investigate the use of language-universal phonological representations which improves average accuracy (absolute) by 36% when transferring between languages that use different scripts.
On Resolving Ambiguous Anaphoric Expressions in Imperative Discourse
Authors: Sarathy Vasanth , Scheutz Matthias
Published: August 2019

Anaphora resolution is a central problem in natural language understanding. We study a subclass of this problem involving object pronouns when they are used in simple imperative sentences (e.g., “pick it up.”). Specifically, we address cases where situational and contextual information is required to interpret these pronouns. Current state-of-the art statisticallydriven coreference systems and knowledge-based reasoning systems are insufficient to address these cases. In this paper, we introduce, with examples, a general class of situated anaphora resolution problems, propose a proof-of-concept system for disambiguating situated pronouns, and discuss some general types of reasoning that might be needed.
Learning Semantic Representations for Novel Words: Leveraging Both Form and Context
Authors: Schick Timo , Schütze Hinrich
Published: August 2019

Word embeddings are a key component of high-performing natural language processing (NLP) systems, but it remains a challenge to learn good representations for novel words on the fly, i.e., for words that did not occur in the training data. The general problem setting is that word embeddings are induced on an unlabeled training corpus and then a model is trained that embeds novel words into this induced embedding space. Currently, two approaches for learning embeddings of novel words exist: (i) learning an embedding from the novel word’s surface-form (e.g., subword n-grams) and (ii) learning an embedding from the context in which it occurs. In this paper, we propose an architecture that leverages both sources of information – surface-form and context – and show that it results in large increases in embedding quality. Our architecture obtains state-of-the-art results on the Definitional Nonce and Contextual Rare Words datasets. As input, we only require an embedding set and an unlabeled corpus for training our architecture to produce embeddings appropriate for the induced embedding space. Thus, our model can easily be integrated into any existing NLP system and enhance its capability to handle novel words.
Challenges in the Automatic Analysis of Students’ Diagnostic Reasoning
Authors: Schulz Claudia , Meyer Christian M. , Gurevych Iryna
Published: August 2019

Diagnostic reasoning is a key component of many professions. To improve students’ diagnostic reasoning skills, educational psychologists analyse and give feedback on epistemic activities used by these students while diagnosing, in particular, hypothesis generation, evidence generation, evidence evaluation, and drawing conclusions. However, this manual analysis is highly time-consuming. We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification. We create the first corpus for this task, comprising diagnostic reasoning selfexplanations of students from two domains annotated with epistemic activities. Based on insights from the corpus creation and the task’s characteristics, we discuss three challenges for the automatic identification of epistemic activities using AI methods: the correct identification of epistemic activity spans, the reliable distinction of similar epistemic activities, and the detection of overlapping epistemic activities. We propose a separate performance metric for each challenge and thus provide an evaluation framework for future research. Indeed, our evaluation of various state-of-the-art recurrent neural network architectures reveals that current techniques fail to address some of these challenges.
Analysis of Joint Multilingual Sentence Representations and Semantic K-Nearest Neighbor Graphs
Authors: Schwenk Holger , Kiela Douwe , Douze Matthijs
Published: August 2019

Multilingual sentence and document representations are becoming increasingly important. We build on recent advances in multilingual sentence encoders, with a focus on efficiency and large-scale applicability. Specifically, we construct and investigate the k-nn graph over the joint space of 566 million news sentences in seven different languages. We show excellent multilingual retrieval quality on the UN corpus of 11.3M sentences, which extends to the zero-shot case where we have never seen a language. We provide a detailed analysis of both the multilingual sentence encoder for twenty-one European languages and the learned graph. Our sentence encoder is language agnostic and supports code switching.
DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization
Authors: Shi Jiaxin , Liang Chen , Hou Lei , Li Juanzi , Liu Zhiyuan , Zhang Hanwang
Published: August 2019

We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization. Given any document-summary pair, we estimate a salience score, which is modeled using an attention-based deep neural network, to represent the salience degree of the summary for yielding the document. We devise a contrastive training strategy to learn the salience estimation network, and then use the learned salience score as a guide and iteratively extract the most salient sentences from the document as our generated summary. In experiments, our model not only achieves state-of-the-art ROUGE scores on CNN/Daily Mail dataset, but also shows strong robustness in the out-of-domain test on DUC2007 test set. Moreover, our model reaches a ROUGE-1 F-1 score of 39.41 on CNN/Daily Mail test set with merely 1/100 training set, demonstrating a tremendous data efficiency.
Towards Sentence-Level Brain Decoding with Distributed Representations
Authors: Sun Jingyuan , Wang Shaonan , Zhang Jiajun , Zong Chengqing
Published: August 2019

Decoding human brain activities based on linguistic representations has been actively studied in recent years. However, most previous studies exclusively focus on word-level representations, and little is learned about decoding whole sentences from brain activation patterns. This work is our effort to mend the gap. In this paper, we build decoders to associate brain activities with sentence stimulus via distributed representations, the currently dominant sentence representation approach in natural language processing (NLP). We carry out a systematic evaluation, covering both widely-used baselines and state-of-the-art sentence representation models. We demonstrate how well different types of sentence representations decode the brain activation patterns and give empirical explanations of the performance difference. Moreover, to explore how sentences are neurally represented in the brain, we further compare the sentence representation’s correspondence to different brain areas associated with high-level cognitive functions. We find the supervised structured representation models most accurately probe the language atlas of human brain. To the best of our knowledge, this work is the first comprehensive evaluation of distributed sentence representations for brain decoding. We hope this work can contribute to decoding brain activities with NLP representation models, and understanding how linguistic items are neurally represented.
A Grammar-Based Structural CNN Decoder for Code Generation
Authors: Sun Zeyu , Zhu Qihao , Mou Lili , Xiong Yingfei , Li Ge , Zhang Lu
Published: August 2019

Code generation maps a program description to executable source code in a programming language. Existing approaches mainly rely on a recurrent neural network (RNN) as the decoder. However, we find that a program contains significantly more tokens than a natural language sentence, and thus it may be inappropriate for RNN to capture such a long sequence. In this paper, we propose a grammar-based structural convolutional neural network (CNN) for code generation. Our model generates a program by predicting the grammar rules of the programming language; we design several CNN modules, including the tree-based convolution and pre-order convolution, whose information is further aggregated by dedicated attentive pooling layers. Experimental results on the HearthStone benchmark dataset show that our CNN code generator significantly outperforms the previous state-of-the-art method by 5 percentage points; additional experiments on several semantic parsing tasks demonstrate the robustness of our model. We also conduct in-depth ablation test to better understand each component of our model.
A Hierarchical Framework for Relation Extraction with Reinforcement Learning
Authors: Takanobu Ryuichi , Zhang Tianyang , Liu Jiexi , Huang Minlie
Published: August 2019

Most existing methods determine relation types only after all the entities have been recognized, thus the interaction between relation types and entity mentions is not fully modeled. This paper presents a novel paradigm to deal with relation extraction by regarding the related entities as the arguments of a relation. We apply a hierarchical reinforcement learning (HRL) framework in this paradigm to enhance the interaction between entity mentions and relation types. The whole extraction process is decomposed into a hierarchy of two-level RL policies for relation detection and entity extraction respectively, so that it is more feasible and natural to deal with overlapping relations. Our model was evaluated on public datasets collected via distant supervision, and results show that it gains better performance than existing methods and is more powerful for extracting overlapping relations1.
CompareLDA: A Topic Model for Document Comparison
Authors: Tkachenko Maksim , Lauw Hady W.
Published: August 2019

A number of real-world applications require comparison of entities based on their textual representations. In this work, we develop a topic model supervised by pairwise comparisons of documents. Such a model seeks to yield topics that help to differentiate entities along some dimension of interest, which may vary from one application to another. While previous supervised topic models consider document labels in an independent and pointwise manner, our proposed Comparative Latent Dirichlet Allocation (CompareLDA) learns predictive topic distributions that comply with the pairwise comparison observations. To fit the model, we derive a maximum likelihood estimation method via augmented variational approximation algorithm. Evaluation on several public datasets underscores the strengths of CompareLDA in modelling document comparisons.
A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context
Authors: Udagawa Takuma , Aizawa Akiko
Published: August 2019

Common grounding is the process of creating, repairing and updating mutual understandings, which is a critical aspect of sophisticated human communication. However, traditional dialogue systems have limited capability of establishing common ground, and we also lack task formulations which introduce natural difficulty in terms of common grounding while enabling easy evaluation and analysis of complex models. In this paper, we propose a minimal dialogue task which requires advanced skills of common grounding under continuous and partially-observable context. Based on this task formulation, we collected a largescale dataset of 6,760 dialogues which fulfills essential requirements of natural language corpora. Our analysis of the dataset revealed important phenomena related to common grounding that need to be considered. Finally, we evaluate and analyze baseline neural models on a simple subtask that requires recognition of the created common ground. We show that simple baseline models perform decently but leave room for further improvement. Overall, we show that our proposed task will be a fundamental testbed where we can train, evaluate, and analyze dialogue system’s ability for sophisticated common grounding.
Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding
Authors: Wang Peifeng , Han Jialong , Li Chenliang , Pan Rong
Published: August 2019

Knowledge graph embedding aims at modeling entities and relations with low-dimensional vectors. Most previous methods require that all entities should be seen during training, which is unpractical for real-world knowledge graphs with new entities emerging on a daily basis. Recent efforts on this issue suggest training a neighborhood aggregator in conjunction with the conventional entity and relation embeddings, which may help embed new entities inductively via their existing neighbors. However, their neighborhood aggregators neglect the unordered and unequal natures of an entity’s neighbors. To this end, we summarize the desired properties that may lead to effective neighborhood aggregators. We also introduce a novel aggregator, namely, Logic Attention Network (LAN), which addresses the properties by aggregating neighbors with both rules- and network-based attention weights. By comparing with conventional aggregators on two knowledge graph completion tasks, we experimentally validate LAN’s superiority in terms of the desired properties.
Chinese NER with Height-Limited Constituent Parsing
Authors: Wang Rui , Xin Xin , Chang Wei , Ming Kun , Li Biao , Fan Xin
Published: August 2019

In this paper, we investigate how to improve Chinese named entity recognition (NER) by jointly modeling NER and constituent parsing, in the framework of neural conditional random fields (CRF). We reformulate the parsing task to heightlimited constituent parsing, by which the computational complexity can be significantly reduced, and the majority of phrase-level grammars are retained. Specifically, an unified model of neural semi-CRF and neural tree-CRF is proposed, which simultaneously conducts word segmentation, part-ofspeech (POS) tagging, NER, and parsing. The challenge comes from how to train and infer the joint model, which has not been solved previously. We design a dynamic programming algorithm for both training and inference, whose complexity is O(n·4h), where n is the sentence length and h is the height limit. In addition, we derive a pruning algorithm for the joint model, which further prunes 99.9% of the search space with 2% loss of the ground truth data. Experimental results on the OntoNotes 4.0 dataset have demonstrated that the proposed model outperforms the state-of-the-art method by 2.79 points in the F1-measure.
A Multi-Agent Communication Framework for Question-Worthy Phrase Extraction and Question Generation
Authors: Wang Siyuan , Wei Zhongyu , Fan Zhihao , Liu Yang , Huang Xuanjing
Published: August 2019

Question generation aims to produce questions automatically given a piece of text as input. Existing research follows a sequence-to-sequence fashion that constructs a single question based on the input. Considering each question usually focuses on a specific fragment of the input, especially in the scenario of reading comprehension, it is reasonable to identify the corresponding focus before constructing the question. In this paper, we propose to identify question-worthy phrases first and generate questions with the assistance of these phrases. We introduce a multi-agent communication framework, taking phrase extraction and question generation as two agents, and learn these two tasks simultaneously via message passing mechanism. The results of experiments show the effectiveness of our framework: we can extract question-worthy phrases, which are able to improve the performance of question generation. Besides, our system is able to extract more than one question worthy phrases and generate multiple questions accordingly.
Review of preparation and application of copper–steel bimetal composites
Authors: Wang Yiran , Gao Yimin , Li Yefei , Zhai Wenyan , Sun Liang , Zhang Chao
Published: August 2019

With the rapid development of modern science and industrial technology, single metals or alloys are restricted by working conditions and cost. It is challenging to meet the requirements for current applications. Copper (Cu)–steel bimetal composites, composed of copper and steel, have performances of the two metals and low cost. They have drawn extensive attention worldwide and have been developed by many scholars for many years. The new preparations are studies to promote performances of the copper–steel bimetal composites and many literatures have also reported different applications of the copper–steel bimetal composites. Up to today, the developing copper–steel bimetal composites have made a great progress. This paper is mainly concerned with the progress of research on the preparation of copper–steel bimetal composites. The preparations are classified according to the forming state of copper and steel. Furthermore, the industrial applications of copper–steel bimetal composites and the prospects of future development are introduced.
Mechanical properties of chemically treated woven banana/polyvinyl alcohol composites
Authors: Gunge Amaresh , Kivade Sangshetty Bheemanna , Nagamadhu Mahadevappa , Rajole Sangamesh
Published: August 2019

The present work investigated the effect of chemical treatment on the mechanical properties of plain-woven banana fabrics reinforced with a polyvinyl alcohol (PVA) biodegradable matrix. Woven banana fabrics were chemically treated with different concentrations (0·5, 1·0, 2·0, 3·0 and 4·0%) for 4 h at room temperature. The banana fabrics and PVA were used in ratios of 55 and 45% weight fractions, respectively. Composites were prepared using the hand-layup method. The samples were tested according to different ASTM standards for tensile, flexural and impact strength. The results showed that tensile, flexural and impact properties improved with potassium permanganate (KMnO4) treatment. The fabrics treated with 1·0% potassium permanganate showed very good mechanical properties compared with the untreated fabrics and the 0·5, 2·0, 3·0 and 4·0% treated fabrics. The 1% treated fabric composite showed a 68·07% increase in tensile strength compared with the untreated fabric composite. Scanning electron microscopy revealed that the 1% treated fabric had better interfacial bonding between the fabric and matrix. This contributes to improvement in the mechanical properties of the composite.
Machining effects on delamination failure in milling MD-CFRPs with uncoated carbide tools
Authors: Ozkan Dervis , Gok Mustafa Sabri , Gokkaya Hasan , Karaoglanli Abdullah Cahit
Published: August 2019

As one of the main failure mechanisms emerging in the milling of carbon (C)-fiber-reinforced polymer (CFRP) materials, delamination is primarily affected by cutting tool material and geometry, machining parameters and the dynamic loads arising during the machining process. In this study, machinability tests were performed without the use of coolants to examine the effect of machining parameters on multidirectional CFRP composite workpieces, which have a wide application in industries. The tests were applied on a computer numerically controlled vertical processing center at three different cutting speeds (100, 200 and 300 m/min), three different feed rates (0·05, 0·15 and 0·25 mm/tooth) and constant cutting depth (1 mm). Following the tests, the changes in the workpiece were examined in terms of surface roughness, cutting forces, tool wear and delamination failures. The average surface roughness increased with increasing feed rate. Cutting forces also increased with increasing cutting speed, which caused improvements on average surface roughness values. As a machining parameter, feed rate was found to be a determining factor in the machining of CFRP composite materials.
Monopile rotation under complex cyclic lateral loading in sand
Authors: Richards Iona A. , Byrne Byron W. , Houlsby Guy T.
Published: August 2019

Monopiles supporting offshore wind turbines experience combined moment and horizontal loading which is both cyclic and complex – continuously varying in amplitude, direction and frequency. The accumulation of rotation with cyclic loading (ratcheting) is a key concern for monopile designers and has been explored in previous experimental studies, where constant-amplitude cyclic tests have shown rotation to accumulate as a power-law with cycle number. This paper presents results from laboratory tests in dry sand, which explore the rotation response to constant and variable amplitude, unidirectional and multidirectional cyclic loading. The tests are designed to inform model development and provide insight into key issues relevant to monopile design. Unidirectional tests show behaviour consistent with previous studies and provide a basis for interpreting more complex tests; multidirectional tests provide new insight into the monopile response to multidirectional cyclic loading; and multi-amplitude storm tests highlight salient features of the response to realistic loading. Tests are conducted in both very loose and dense sand, where the behaviour is found to be qualitatively similar.
Investigation of moisture transport in cement-based materials using low-field nuclear magnetic resonance imaging
Authors: Zhao Haitao , Wu Xia , Huang Yuyu , Zhang Peng , Tian Qian , Liu Jiaping
Published: August 2019

Moisture transport has a great impact on concrete durability. In this study, a hard-pulse one-dimensional frequency coding sequence of low-field nuclear magnetic resonance (LF-NMR) imaging was applied to investigate moisture transport in cement-based materials (CBMs) considering the influences of the water/binder (w/b) ratio, sand/cement (s/c) ratio and contents of silica fume (SF) and super-absorbent polymer (SAP). A weighing method was also applied and the results were compared with the LF-NMR imaging measurements. The results showed that the moisture distribution curves can be divided into three stages by height. In addition, it was found that the moisture absorption height (h) increased with time and the growth rate of water absorption proceeded from fast to slow. Additionally, as the w/b ratio of the sample was increased, h increased. The value of h decreased with increasing s/c ratio and increased with an increase in SF content. When SAP was mixed into the CBMs, the values of h increased with 0·15% SAP addition but decreased with the addition of 0·30% SAP. The LF-NMR imaging measurements were in agreement with the results of the weighing method.
Alternativas de modelos de negocios para las mipymes colombianas desde la responsabilidad social empresarial, el comercio justo, los negocios inclusivos y el valor compartido
Authors: Hernández Bernal Patricia
Published: August 2019

Propósito: El objetivo de este artículo es proponer una alternativa estratégica a los empresarios de las Mipymes y a los nuevos emprendedores colombianos, a partir de la responsabilidad social empresarial (RSE), el comercio justo, los negocios inclusivos y el valor compartido, como opción de nuevas formas de producción y creación de desarrollo. Descripción: Se presenta de forma independiente cada uno de los temas haciendo un recorrido sucinto con el fin de sensibilizar al lector en cuanto a nuevas prácticas de hacer negocios. Punto de vista: se considera pertinente y oportuno establecer una reflexión académica, que lleva a la visualización de los campos de acción de la RSE en el caso colombiano. Es así como es necesaria una investigación que, en primera instancia, brinde la claridad sobre las raíces teóricas de la RSE y al mismo tiempo se construya un marco teórico, en donde se pueda identificar los conceptos con los que guarda relación. Conclusiones: El compromiso de los nuevos empresarios o empresarios renovados,  debe ser pensar en el progreso de la calidad de vida sus empleados y el buen vivir de sus familias, logística inversa, sostenibilidad de la cadena de suministro, entre otros aspectos.
İlkokul Matematik Dersinde Öz Değerlendirmenin Etkililiği
Authors: GÜLTEKİN Mehmet , DEMİR Zülfiye Melis
Published: August 2019

Bu araştırmanın amacı, ilkokul dördüncü sınıf matematik dersinde ölçme
ve değerlendirmede öz değerlendirmenin etkililiğini belirlemektir. Karma desen
benimsenerek gerçekleştirilen araştırma, Eskişehir ilinde orta sosyoekonomik
düzeydeki bir ilkokulun 4. sınıfında gerçekleştirilmiştir. Araştırmanın
katılımcıları, amaçlı örnekleme yöntemlerinden ölçüt örnekleme ile
belirlenmiştir. Araştırmada nicel verilerin toplanmasında Öz Değerlendirme
Tutum ölçeği, nitel verilerin toplanmasında ise öğrenciler ve öğretmen için
ayrı ayrı oluşturulmuş yarı yapılandırılmış görüşme formu kullanılmıştır.  Nicel verilerin analizinde betimsel
istatistik yöntemleri, nitel verilerin analizinde ise içerik analizi
kullanılmıştır. Araştırma bulgularına göre öz değerlendirme tutum ölçeği ön
test ve son test puanları arasında son test lehine anlamlı bir farklılık
bulunmuştur. Sınıf öğretmeni, öz değerlendirmenin “öğrenciye dönüt sağlama” ve
“öğrencilerde farkındalık sağlama” açısından yararlı olduğunu, “öğrencilerin
bireysel farklılıkları”, “zaman” ve “öğrenci cevaplarının güvenirliği”
açısından ise sınırlıklarının olduğunu belirtmiştir. Öğrenciler ise öz değerlendirme formlarının matematik
dersinde kullanılmasının “derse ilgi ve başarıyı arttırma”, “kendine ilişkin
farkındalık oluşturma” ve “öz güven sağlama” gibi olumlu katkılarının olduğunu
belirtmişlerdir.
Varlığında Çocuk, Yokluğunda Yetişkin Olmak: Baba Kaybı, Kardeş İlişkileri ve Aile Yılmazlığı
Authors: ÖZBAY Yaşar , AYDOĞAN Didem
Published: August 2019

Bu
çalışmanın amacı, son beş yıl içinde babasını kaybetmiş bireylerde aile
yılmazlığının derinlemesine incelenerek, kardeş ilişkilerinin rolünü
değerlendirmektir. Bir ebeveynin kaybı pek çok kişinin yaşamında önemli dönüm
noktalarından biri olmaktadır. Yaşanan kayıp karşısında değişen aile sistemine,
aile üyeleri var olan kaynakları kullanarak üstesinden gelmeye
çalışmaktadırlar. Bu bağlamda ailede ki etkileşim süreçleri ve aile alt sistemi
olarak kardeşler arasındaki ilişkinin niteliği ve rolü önemli faktörlerdir.
Araştırmada amaçlı örnekleme yöntemlerinden biri olan ölçüt örnekleme yöntemi
kullanılmıştır. Araştırmaya bu kapsamda, (I)
son beş yıl içinde babayı kaybetmiş olma, (II)
bir üniversitede öğrenimine devam etme, (III) aynı ev ortamını paylaştığı en az bir kardeşe sahip olma, (IV) birlikte yaşadığı ebeveynin hayatta
olması, (V) şuan yaşayan ebeveynin
yeniden evlenmemesi ölçüt olarak belirlenen
10 kişi dâhil edilmiştir. Araştırmada veriler yarı yapılandırılmış görüşme
formu aracılığı ile toplanmıştır. Verilerin analizinde, betimsel analiz
tekniğinden yararlanılmıştır. Sonuçlar yaşanılan baba kaybından sonra aile
yılmazlığını değerlendirmede, ailede var olan yönetim şekillerinin ve aile
inanç sistemlerinin,  aynı zamanda
kardeşler arasındaki yakın, destekleyici ilişki yapılarının ve rol değişiminin,
ailede pozitif gelişimi sağlamada önemli ve güçlü nitelikler olduğunu ortaya
koymaktadır. Elde edilen bu sonuçlar özellikle de kültürel bağlamda aile de
yaşanılan kayıp sonrasında ailelerin güçlü özelliklerinin belirlenmesi ve
önemli bir ilişki türü olan kardeş ilişkilerin anlaşılması açısından ışık
tutucu niteliktedir. Araştırmanın sonuçlarına dayalı olarak pozitif aile
gelişimini sağlayan, aile yılmazlığını güçlendirmede kültürel bağlamda
müdahale, planlamalar ve öneriler sunulmuştur.
ENTRE O VISÍVEL E O INVISÍVEL: A IMAGEM COMO MATERIALIZAÇÃO DA ENCANTARIA
Authors: Paradiso Silvio Ruiz , Vasconcelos Maria Fernanda Casanova
Published: August 2019

O presente trabalho é fruto de uma pesquisa em poéticas visuais motivada pela busca por utilizar a arte como representação iconográfica da encantaria. Foram escolhidos como tema os caboclos e os encantados, seres míticos e invisíveis, presentes no Tambor de Mina, religião afro-brasileira fortemente encontrada no Maranhão e no Pará, que se apresentam em nosso mundo através do transe dos membros de tal crença. Para tanto, foi realizado um estudo na Casa de Mina de Yemanjá e Ogum, em Maringá-PR, com os próprios encantados, bem como com praticantes dos ritos de matriz africana. O foco se deu nas investigações acerca de suas identidades perante a religião, e também nas pesquisas sobre as possibilidades de se construir a imagem como um desdobramento material do espírito imaterial, tendo como ponte diálogos, vivências e percepções. Como resultado, têm-se retratos pintados com pastel oleoso sobre papel, representando figurativamente as entidades pesquisadas.
QUALIDADE DE VIDA NO CONTEXTO DE PACIENTES COM HIV/AIDS: UM ESTUDO COMPARATIVO
Authors: Martins Neto Carlos , Pires Erick Matheus Correa , Brito Caroline de Souto , Beserra Olga Lorena Maluf Guará , Silva Junior João Ferreira , Mota João Victor , Caldas Renata Trajano Jorge
Published: August 2019

Comparar a percepção da qualidade de vida de pessoas HIV+ com pessoas sem o diagnóstico para o HIV/AIDS. Pesquisa quantitativa, analítica, com comparação entre duas amostras de 240 indivíduos, sendo 120 HIV positivo e 120 HIV negativo. Utilizou-se como instrumento o questionário WHOQoL-HIV-bref e um segundo desenvolvido pelos pesquisadores. Realizou-se análise descritiva dos dados por meio do software Stata, versão 14.0 e a análise estatística pelo teste qui-quadrado e t de student (p