The goal of sex and gender analysis is to promote rigorous, reproducible and responsible
science. Incorporating sex and gender analysis into experimental design has enabled …

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 …

State-of-the-art methods for learning cross-lingual word embeddings have relied on
bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel …

We present a new technique for learning visual-semantic embeddings for cross-modal
retrieval. Inspired by hard negative mining, the use of hard negatives in structured …

Abstract Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states
in texts. Various Deep Learning (DL) methods have developed rapidly, and they have …

State-of-the-art methods for learning cross-lingual word embeddings have relied on
bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel …

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 developing …

Most methods to learn bilingual word embeddings rely on large parallel corpora, which is
difficult to obtain for most language pairs. This has motivated an active research line to relax …

In e-commerce, user reviews can play a significant role in determining the revenue of an
organisation. Online users rely on reviews before making decisions about any product and …

Current state-of-the-art NMT systems use large neural networks that are not only slow to
train, but also often require many heuristics and optimization tricks, such as specialized …