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

A common semantic space for monolingual and cross-lingual meta-embeddings

This master’s thesis presents a new technique for creating monolingual and cross-lingual meta-embeddings. Our method integrates multiple word embeddings created from complementary techniques, textual sources, knowledge bases and languages. Existing word vectors are projected to a common semantic space using linear transformations and averaging. With our method the resulting meta-embeddings maintain the dimensionality of the original embeddings without losing information while dealing with the out-of-vocabulary (OOV) problem. Furthermore, empirical evaluation demonstrates the effectiveness of our technique with respect to previous work on various intrinsic and extrinsic multilingual evaluations

Poznawcze przesłanki semiozy zorientowanej na mit

This article addresses the cognitive premises of designation units denoting mythic concepts in a variety of texts and discourses. The article focuses on myth-oriented semiosis as a cognitive and cultural phenomenon reflected in the semantic transformations of lingual signs, resulting in the development of noematic senses relevant to the states of affairs in diverse worldviews or modelled alternative realities. This article provides an analysis of the basic cognitive models and procedures responsible for irrational cognition. The reconstructed cognitive models are then discussed in terms of their correspondence with the universal patterns of open system interaction and information exchange.Ten artykuł dotyczy poznawczych przesłanek jednostek desygnacyjnych oznaczających mityczne pojęcia w różnych tekstach i dyskursach. Artykuł koncentruje się na semiozie zorientowanej na mit jako zjawisku poznawczym i kulturowym odzwierciedlonym w semantycznych przekształceniach znaków językowych, co skutkuje rozwojem noematycznych zmysłów związanych ze stanami rzeczy w różnych światopoglądach lub modelowanych alternatywnych rzeczywistościach. Ten artykuł zawiera analizę podstawowych modeli i procedur poznawczych odpowiedzialnych za irracjonalne poznanie. Zrekonstruowane modele poznawcze są następnie omawiane pod kątem ich zgodności z uniwersalnymi wzorcami interakcji otwartego systemu i wymiany informacji