Text‐based question answering (QA) is a challenging task which aims at finding short
concrete answers for users' questions. This line of research has been widely studied with …

Question Answering is a crucial natural language processing task. This field of research has
attracted a sudden amount of interest lately due mainly to the integration of the deep …

Abstract Recent advancements in Large Vision-Language Models (VLMs) have shown great
promise in natural image domains allowing users to hold a dialogue about given visual …

Recurrent neural networks and exceedingly Long short-term memory (LSTM) have been
investigated intensively in recent years due to their ability to model and predict nonlinear …

Ranking models lie at the heart of research on information retrieval (IR). During the past
decades, different techniques have been proposed for constructing ranking models, from …

Aspect-based sentiment analysis (ABSA) tries to predict the polarity of a given document
with respect to a given aspect entity. While neural network architectures have been …

Obstacles hindering the development of capsule networks for challenging NLP applications
include poor scalability to large output spaces and less reliable routing processes. In this …

Word embeddings that consider context have attracted great attention for various natural
language processing tasks in recent years. In this paper, we utilize contextualized word …

Deep learning has demonstrated tremendous potential for Automatic Text Scoring (ATS)
tasks. In this paper, we describe a new neural architecture that enhances vanilla neural …

The dominant neural architectures in question answer retrieval are based on recurrent or
convolutional encoders configured with complex word matching layers. Given that recent …