Constructing Datasets for Multi-hop Reading Comprehension Across Documents

Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document. Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently there exist no resources to train and test this capability. We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods. In our task, a model learns to seek and combine evidence - effectively performing multi-hop (alias multi-step) inference. We devise a methodology to produce datasets for this task, given a collection of query-answer pairs and thematically linked documents. Two datasets from different domains are induced, and we identify potential pitfalls and devise circumvention strategies. We evaluate two previously proposed competitive models and find that one can integrate information across documents. However, both models struggle to select relevant information, as providing documents guaranteed to be relevant greatly improves their performance. While the models outperform several strong baselines, their best accuracy reaches 42.9% compared to human performance at 74.0% - leaving ample room for improvement.Comment: This paper directly corresponds to the TACL version (https://transacl.org/ojs/index.php/tacl/article/view/1325) apart from minor changes in wording, additional footnotes, and appendice

mARC: Memory by Association and Reinforcement of Contexts

This paper introduces the memory by Association and Reinforcement of Contexts (mARC). mARC is a novel data modeling technology rooted in the second quantization formulation of quantum mechanics. It is an all-purpose incremental and unsupervised data storage and retrieval system which can be applied to all types of signal or data, structured or unstructured, textual or not. mARC can be applied to a wide range of information clas-sification and retrieval problems like e-Discovery or contextual navigation. It can also for-mulated in the artificial life framework a.k.a Conway "Game Of Life" Theory. In contrast to Conway approach, the objects evolve in a massively multidimensional space. In order to start evaluating the potential of mARC we have built a mARC-based Internet search en-gine demonstrator with contextual functionality. We compare the behavior of the mARC demonstrator with Google search both in terms of performance and relevance. In the study we find that the mARC search engine demonstrator outperforms Google search by an order of magnitude in response time while providing more relevant results for some classes of queries

Crowdsourcing Multiple Choice Science Questions

We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers. Generating these questions can be difficult without trading away originality, relevance or diversity in the answer options. Our method addresses these problems by leveraging a large corpus of domain-specific text and a small set of existing questions. It produces model suggestions for document selection and answer distractor choice which aid the human question generation process. With this method we have assembled SciQ, a dataset of 13.7K multiple choice science exam questions (Dataset available at http://allenai.org/data.html). We demonstrate that the method produces in-domain questions by providing an analysis of this new dataset and by showing that humans cannot distinguish the crowdsourced questions from original questions. When using SciQ as additional training data to existing questions, we observe accuracy improvements on real science exams.Comment: accepted for the Workshop on Noisy User-generated Text (W-NUT) 201

Novel linguistic steganography based on character-level text generation

With the development of natural language processing, linguistic steganography has become a research hotspot in the field of information security. However, most existing linguistic steganographic methods may suffer from the low embedding capacity problem. Therefore, this paper proposes a character-level linguistic steganographic method (CLLS) to embed the secret information into characters instead of words by employing a long short-term memory (LSTM) based language model. First, the proposed method utilizes the LSTM model and large-scale corpus to construct and train a character-level text generation model. Through training, the best evaluated model is obtained as the prediction model of generating stego text. Then, we use the secret information as the control information to select the right character from predictions of the trained character-level text generation model. Thus, the secret information is hidden in the generated text as the predicted characters having different prediction probability values can be encoded into different secret bit values. For the same secret information, the generated stego texts vary with the starting strings of the text generation model, so we design a selection strategy to find the highest quality stego text from a number of candidate stego texts as the final stego text by changing the starting strings. The experimental results demonstrate that compared with other similar methods, the proposed method has the fastest running speed and highest embedding capacity. Moreover, extensive experiments are conducted to verify the effect of the number of candidate stego texts on the quality of the final stego text. The experimental results show that the quality of the final stego text increases with the number of candidate stego texts increasing, but the growth rate of the quality will slow down

Authorship Attribution Using Principal Component Analysis and Nearest Neighbor Rule for Neural Networks

Feature extraction is a common problem in statistical pattern recognition. It refers to a process whereby a data space is transformed into a feature space that, in theory, has exactly the same dimension as the original data space. However, the transformation is designed in such a way that the data set may be represented by a reduced number of "effective" features and yet retain most of the intrinsic information content of the data; in other words, the data set undergoes a dimensionality reduction. Principal component analysis is one of these processes. In this paper the data collected by counting selected syntactic characteristics in around a thousand paragraphs of each of the sample books underwent a principal component analysis. To make a comparison, the original data is also processed. Authors of texts identified with higher success by the competitive neural networks, which use principal components. The process repeated on another group of authors, and similar results are obtained

Atribuição de autoria em micro-mensagens

Orientadores: Ariadne Maria Brito Rizzoni Carvalho, Anderson de Rezende RochaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação CientíficaResumo: Com o crescimento continuo do uso de midias sociais, a atribuição de autoria tem um papel imortante na prevenção dos crimes cibernéticos e na análise de rastros online deixados por assediadores, \textit{bullies}, ladrões de identidade entre outros. Nesta dissertação, nós propusemos um método para atribuição de autoria que é de cem a mil vezes mais rápido que o estado da arte. Nós também obtivemos uma acurácia 65\% na classificação de 50 autores. O método proposto se baseia numa representação de caracteristicas escalável utilizando os padrões das mensagens dos micro-blogs, e também nos utilizamos de um classificador de padrões customizado para lidar com grandes quantidades de dados e alta dimensionalidade. Por fim, nós discutimos a redução do espaço de busca na análise de centenas de suspeitos online e milões de micro mensagens online, o que torna essa abordagem valiosa para forense digital e aplicação das leisAbstract: With the ever-growing use of social media, authorship attribution plays an important role in avoiding cybercrime, and helping the analysis of online trails left behind by cyber pranks, stalkers, bullies, identity thieves and alike. In this dissertation, we propose a method for authorship attribution in micro blogs with efficiency one hundred to a thousand times faster than state-of-the-art counterparts. We also achieved a accuracy of 65% when classifying texts from 50 authors. The method relies on a powerful and scalable feature representation approach taking advantage of user patterns on micro-blog messages, and also on a custom-tailored pattern classifier adapted to deal with big data and high-dimensional data. Finally, we discuss search space reduction when analysing hundreds of online suspects and millions of online micro messages, which makes this approach invaluable for digital forensics and law enforcementMestradoCiência da ComputaçãoMestre em Ciência da Computaçã