Generalized Tuning of Distributional Word Vectors for Monolingual and Cross-Lingual Lexical Entailment

Abstract

Lexical entailment (LE; also known as hyponymy-hypernymy or is-a relation) is a core asymmetric lexical relation that supports tasks like taxonomy induction and text generation. In this work, we propose a simple and effective method for fine-tuning distributional word vectors for LE. Our Generalized Lexical ENtailment model (GLEN) is decoupled from the word embedding model and applicable to any distributional vector space. Yet-unlike existing retrofitting models-it captures a general specialization function allowing for LE-tuning of the entire distributional space and not only the vectors of words seen in lexical constraints. Coupled with a multilingual embedding space, GLEN seamlessly enables cross-lingual LE detection. We demonstrate the effectiveness of GLEN in graded LE and report large improvements (over 20% in accuracy) over state-ofthe-art in cross-lingual LE detection.

Improving Neural Political Statement Classification with Class Hierarchical Information

“…Depending on the properties of the annotation, the task is either single-label or multi-label text classification. In single label text classification, each text is assigned exactly one label, which is used in NLP applications where the labels are mutually exclusive, such as in entailment or stance detection (Kim, 2014;Glavaš and Vulić, 2019;Kennedy et al, 2019;Li and Caragea, 2019). In contrast, multi-label text classification assigns any number of categories to a text, which is better suited for tasks where the categories are overlapping or describe complementary aspects, e.g.…”

Patch-Based Identification of Lexical Semantic Relations

“…First, specialized similarity measures can be defined to distinguish different relations [29]. Another solution is to specialize word embeddings for particular relations using external knowledge [9,36]. However, these methods are one-relation specific and cannot differentiate between multiple semantic relations at a time.…”

“…The most studied lexical semantic relations are synonymy, co-hyponymy, hypernymy, or meronymy, but more exist [37]. Numerous approaches have been proposed to identify one particular semantic relation of interest following either the paradigmatic approach [28,29,33,39], the distributional model [9,31,36,37], or their combination [25,32].…”

“…However, integrating continuous pattern representations within the patch paradigm is not straightforward and requires specific further analysis, which is out of the scope of this paper. But, to overcome the main drawback of the distributional hypothesis that conflates different semantic relations between words [9], we design different multi-task neural learning strategies, as recently introduced by [1], together with their binary classification counterparts.…”