Estimating the semantic similarity between text data is one of the challenging and open
research problems in the field of Natural Language Processing (NLP). The versatility of …

The field of natural language processing has seen impressive progress in recent years, with
neural network models replacing many of the traditional systems. A plethora of new models …

Extensive evaluation on a large number of word embedding models for language
processing applications is conducted in this work. First, we introduce popular word …

Cross-lingual representations of words enable us to reason about word meaning in
multilingual contexts and are a key facilitator of cross-lingual transfer when develo** …

Contextualized representations (eg ELMo, BERT) have become the default pretrained
representations for downstream NLP applications. In some settings, this transition has …

Real-valued word representations have transformed NLP applications; popular examples
are word2vec and GloVe, recognized for their ability to capture linguistic regularities. In this …

Unsupervised machine translation---ie, not assuming any cross-lingual supervision signal,
whether a dictionary, translations, or comparable corpora---seems impossible, but …

Is it possible to learn something without being aware of it? How does emotion influence the
way we think? How can we improve our memory? Fundamentals of Cognition, third edition …

Similarity measures are a vital tool for understanding how language models represent and
process language. Standard representational similarity measures such as cosine similarity …

Distributional semantics is a usage-based model of meaning, based on the assumption that
the statistical distribution of linguistic items in context plays a key role in characterizing their …