Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT

Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new release of BERT (Devlin, 2018) includes a model simultaneously pretrained on 104 languages with impressive performance for zero-shot cross-lingual transfer on a natural language inference task. This paper explores the broader cross-lingual potential of mBERT (multilingual) as a zero shot language transfer model on 5 NLP tasks covering a total of 39 languages from various language families: NLI, document classification, NER, POS tagging, and dependency parsing. We compare mBERT with the best-published methods for zero-shot cross-lingual transfer and find mBERT competitive on each task. Additionally, we investigate the most effective strategy for utilizing mBERT in this manner, determine to what extent mBERT generalizes away from language specific features, and measure factors that influence cross-lingual transfer.Comment: EMNLP 2019 Camera Read

Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding

We construct a multilingual common semantic space based on distributional semantics, where words from multiple languages are projected into a shared space to enable knowledge and resource transfer across languages. Beyond word alignment, we introduce multiple cluster-level alignments and enforce the word clusters to be consistently distributed across multiple languages. We exploit three signals for clustering: (1) neighbor words in the monolingual word embedding space; (2) character-level information; and (3) linguistic properties (e.g., apposition, locative suffix) derived from linguistic structure knowledge bases available for thousands of languages. We introduce a new cluster-consistent correlational neural network to construct the common semantic space by aligning words as well as clusters. Intrinsic evaluation on monolingual and multilingual QVEC tasks shows our approach achieves significantly higher correlation with linguistic features than state-of-the-art multi-lingual embedding learning methods do. Using low-resource language name tagging as a case study for extrinsic evaluation, our approach achieves up to 24.5\% absolute F-score gain over the state of the art.Comment: 10 page

Safeguarding Privacy Through Deep Learning Techniques

Over the last few years, there has been a growing need to meet minimum security and privacy requirements. Both public and private companies have had to comply with increasingly stringent standards, such as the ISO 27000 family of standards, or the various laws governing the management of personal data. The huge amount of data to be managed has required a huge effort from the employees who, in the absence of automatic techniques, have had to work tirelessly to achieve the certification objectives. Unfortunately, due to the delicate information contained in the documentation relating to these problems, it is difficult if not impossible to obtain material for research and study purposes on which to experiment new ideas and techniques aimed at automating processes, perhaps exploiting what is in ferment in the scientific community and linked to the fields of ontologies and artificial intelligence for data management. In order to bypass this problem, it was decided to examine data related to the medical world, which, especially for important reasons related to the health of individuals, have gradually become more and more freely accessible over time, without affecting the generality of the proposed methods, which can be reapplied to the most diverse fields in which there is a need to manage privacy-sensitive information

From Information Overload to Knowledge Graphs: An Automatic Information Process Model

Continuously increasing text data such as news, articles, and scientific papers from the Internet have caused the information overload problem. Collecting valuable information as well as coding the information efficiently from enormous amounts of unstructured textual information becomes a big challenge in the information explosion age. Although many solutions and methods have been developed to reduce information overload, such as the deduction of duplicated information, the adoption of personal information management strategies, and so on, most of the existing methods only partially solve the problem. What’s more, many existing solutions are out of date and not compatible with the rapid development of new modern technology techniques. Thus, an effective and efficient approach with new modern IT (Information Technology) techniques that can collect valuable information and extract high-quality information has become urgent and critical for many researchers in the information overload age. Based on the principles of Design Science Theory, the paper presents a novel approach to tackle information overload issues. The proposed solution is an automated information process model that employs advanced IT techniques such as web scraping, natural language processing, and knowledge graphs. The model can automatically process the full cycle of information flow, from information Search to information Collection, Information Extraction, and Information Visualization, making it a comprehensive and intelligent information process tool. The paper presents the model capability to gather critical information and convert unstructured text data into a structured data model with greater efficiency and effectiveness. In addition, the paper presents multiple use cases to validate the feasibility and practicality of the model. Furthermore, the paper also performed both quantitative and qualitative evaluation processes to assess its effectiveness. The results indicate that the proposed model significantly reduces the information overload and is valuable for both academic and real-world research

A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings

The lack of annotated data in many languages is a well-known challenge within the field of multilingual natural language processing (NLP). Therefore, many recent studies focus on zero-shot transfer learning and joint training across languages to overcome data scarcity for low-resource languages. In this work we (i) perform a comprehensive comparison of state-ofthe-art multilingual word and sentence encoders on the tasks of named entity recognition (NER) and part of speech (POS) tagging; and (ii) propose a new method for creating multilingual contextualized word embeddings, compare it to multiple baselines and show that it performs at or above state-of-theart level in zero-shot transfer settings. Finally, we show that our method allows for better knowledge sharing across languages in a joint training setting.Comment: 7 pages, 6 figure