The choice of token vocabulary affects the performance of machine translation. This paper
aims to figure out what is a good vocabulary and whether one can find the optimal …

The last two decades have seen an exponential increase in the use of the Internet and
social media, which has changed basic human interaction. This has led to many positive …

Due to the explosion of new sources of human language data and the rapid progression of
computational methods for extracting meaning from natural language, language analysis is …

Aspect-based sentiment analysis (ABSA) aims at identifying fine-grained polarity of opinion
associated with a given aspect word. Several existing articles demonstrated promising …

We study the effectiveness of Feature Density (FD) using different linguistically-backed
feature preprocessing methods in order to estimate dataset complexity, which in turn is used …

The increased demand for machine learning applications made companies offer Machine-
Learning-as-a-Service (MLaaS). In MLaaS (a market estimated 8000M USD by 2025), users …

Text-to-SQL parsers map natural language questions to programs that are executable over
tables to generate answers, and are typically evaluated on large-scale datasets like Spider …

This paper explores the question of how predictive uncertainty methods perform in practice
in Natural Language Processing, specifically multi-class and multi-label text classification …

Translation between natural language and source code can help software development by
enabling developers to comprehend, ideate, search, and write computer programs in natural …

The input vocabulary and the representations learned are crucial to the performance of
neural NLP models. Using the full vocabulary results in less explainable and more memory …