Abstract Natural Language Processing (NLP) is a branch of artificial intelligence that
involves the design and implementation of systems and algorithms able to interact through …

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 …

We propose TandA, an effective technique for fine-tuning pre-trained Transformer models for
natural language tasks. Specifically, we first transfer a pre-trained model into a model for a …

Deep multi-view representation learning focuses on training a unified low-dimensional
representation for data with multiple sources or modalities. With the rapidly growing attention …

Large transformer-based language models have been shown to be very effective in many
classification tasks. However, their computational complexity prevents their use in …

IR-based Question Answering (QA) systems typically use a sentence selector to extract the
answer from retrieved documents. Recent studies have shown that powerful neural models …

Answer selection (AS) is a critical subtask of the open‐domain question answering (QA)
problem. The present paper proposes a method called RLAS‐BIABC for AS, which is …

Question answering (QA) enables the system to answer questions automatically. In recent
years, much research has been done in this area. In most methods, question and answer …

Question Answering (QA), a popular and promising technique for intelligent information
access, faces a dilemma about data as most other AI techniques. On one hand, modern QA …