An introduction to deep learning in natural language processing: Models, techniques, and tools
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 …
involves the design and implementation of systems and algorithms able to interact through …
Text‐based question answering from information retrieval and deep neural network perspectives: A survey
Z Abbasiantaeb, S Momtazi - Wiley Interdisciplinary Reviews …, 2021 - Wiley Online Library
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 …
concrete answers for users' questions. This line of research has been widely studied with …
Deep learning-based question answering: a survey
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 …
attracted a sudden amount of interest lately due mainly to the integration of the deep …
Tanda: Transfer and adapt pre-trained transformer models for answer sentence selection
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 …
natural language tasks. Specifically, we first transfer a pre-trained model into a model for a …
Dual fusion-propagation graph neural network for multi-view clustering
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 …
representation for data with multiple sources or modalities. With the rapidly growing attention …
The cascade transformer: an application for efficient answer sentence selection
L Soldaini, A Moschitti - arXiv preprint arXiv:2005.02534, 2020 - arxiv.org
Large transformer-based language models have been shown to be very effective in many
classification tasks. However, their computational complexity prevents their use in …
classification tasks. However, their computational complexity prevents their use in …
Reranking for efficient transformer-based answer selection
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 from retrieved documents. Recent studies have shown that powerful neural models …
RLAS‐BIABC: A Reinforcement Learning‐Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm
H Gharagozlou, J Mohammadzadeh… - Computational …, 2022 - Wiley Online Library
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 …
problem. The present paper proposes a method called RLAS‐BIABC for AS, which is …
A method based on attention mechanism using bidirectional long-short term memory (BLSTM) for question answering
SV Moravvej, MJM Kahaki… - 2021 29th Iranian …, 2021 - ieeexplore.ieee.org
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 …
years, much research has been done in this area. In most methods, question and answer …
Fedmatch: Federated learning over heterogeneous question answering data
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 …
access, faces a dilemma about data as most other AI techniques. On one hand, modern QA …