Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization

Semantic specialization is the process of fine-tuning pre-trained distributional word vectors using external lexical knowledge (e.g., WordNet) to accentuate a particular semantic relation in the specialized vector space. While post-processing specialization methods are applicable to arbitrary distributional vectors, they are limited to updating only the vectors of words occurring in external lexicons (i.e., seen words), leaving the vectors of all other words unchanged. We propose a novel approach to specializing the full distributional vocabulary. Our adversarial post-specialization method propagates the external lexical knowledge to the full distributional space. We exploit words seen in the resources as training examples for learning a global specialization function. This function is learned by combining a standard L2-distance loss with an adversarial loss: the adversarial component produces more realistic output vectors. We show the effectiveness and robustness of the proposed method across three languages and on three tasks: word similarity, dialog state tracking, and lexical simplification. We report consistent improvements over distributional word vectors and vectors specialized by other state-of-the-art specialization frameworks. Finally, we also propose a cross-lingual transfer method for zero-shot specialization which successfully specializes a full target distributional space without any lexical knowledge in the target language and without any bilingual data.Comment: Accepted at EMNLP 201

Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications

[No abstract available

Machine Semiotics

Despite their satisfactory speech recognition capabilities, current speech assistive devices still lack suitable automatic semantic analysis capabilities as well as useful representation of pragmatic world knowledge. Instead, current technologies require users to learn keywords necessary to effectively operate and work with a machine. Such a machine-centered approach can be frustrating for users. However, recognizing a basic difference between the semiotics of humans and machines presents a possibility to overcome this shortcoming: For the machine, the meaning of a (human) utterance is defined by its own scope of actions. Machines, thus, do not need to understand the meanings of individual words, nor the meaning of phrasal and sentence semantics that combine individual word meanings with additional implicit world knowledge. For speech assistive devices, the learning of machine specific meanings of human utterances by trial and error should be sufficient. Using the trivial example of a cognitive heating device, we show that -- based on dynamic semantics -- this process can be formalized as the learning of utterance-meaning pairs (UMP). This is followed by a detailed semiotic contextualization of the previously generated signs.Comment: 37 pages, 4 table

Fully statistical neural belief tracking

This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST). The existing NBT model uses a hand-crafted belief state update mechanism which involves an expensive manual retuning step whenever the model is deployed to a new dialogue domain. We show that this update mechanism can be learned jointly with the semantic decoding and context modelling parts of the NBT model, eliminating the last rule-based module from this DST framework. We propose two different statistical update mechanisms and show that dialogue dynamics can be modelled with a very small number of additional model parameters. In our DST evaluation over three languages, we show that this model achieves competitive performance and provides a robust framework for building resource-light DST models

Zero-shot Multi-Domain Dialog State Tracking Using Descriptive Rules

In this work, we present a framework for incorporating descriptive logical rules in state-of-the-art neural networks, enabling them to learn how to handle unseen labels without the introduction of any new training data. The rules are integrated into existing networks without modifying their architecture, through an additional term in the network’s loss function that penalizes states of the network that do not obey the designed rules.As a case of study, the framework is applied to an existing neuralbased Dialog State Tracker. Our experiments demonstrate that the inclusion of logical rules allows the prediction of unseen labels, without deteriorating the predictive capacity of the original system.Fil: Altszyler Lemcovich, Edgar Jaim. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Brusco, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Basiou, Nikoletta. Sri International; Estados UnidosFil: Byrnes, John. Sri International; Estados UnidosFil: Vergyri, Dimitra. Sri International; Estados Unido

Методы интеллектуальной обработки и представления информации в мультипредметных информационных системах промышленных предприятий

In this paper, the problem of improving the technology of formation and functioning of multi-subject intellectualized information systems of industry is considered. As a solution to this problem, we propose an architecture of multi-subject IS information system of an industrial enterprise; an automated method of formation of a semantic domain model based on the principle of "user as an expert."; a method of formation of cognitive user interfaces adapted for different categories of users; and search method , providing automated query expansion and evaluation of the relevance of search results based on a joint of analysis of the formal mental models and semantic models of a domain with the subtractive relationshipВ работе рассматривается задача формирования единого информационного пространства промышленного предприятия в виде мультипредметной информационной системы промышленного предприятия (МИСПП). Предложена архитектура мультипредметной информационной системы промышленного предприятия, метод автоматизированного формирования семантической модели предметной области информационной системы на основе принципа «пользователь как эксперт», метод формирования когнитивных пользовательских интерфейсов, адаптированных для различных категорий пользователей, и метод поиска, обеспечивающий автоматизированное расширение запроса и оценку релевантности результатов поиска на основе совместного анализа формализованной ментальной модели и семантической модели предметной области с учетом субтрактивных отношений