Answer Extraction for Why Arabic Questions Answering Systems: EWAQ
July 04, 2019 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Fatima T. AL-Khawaldeh
arXiv ID
1907.04149
Category
cs.CL: Computation & Language
Cross-listed
cs.IR
Citations
20
Venue
arXiv.org
Last Checked
4 months ago
Abstract
With the increasing amount of web information, questions answering systems becomes very important to allow users to access to direct answers for their requests. This paper presents an Arabic Questions Answering Systems based on entailment metrics. The type of questions which this paper focuses on is why questions. There are many reasons lead us to develop this system: generally, the lack of Arabic Questions Answering Systems and scarcity Arabic Questions Answering Systems which focus on why questions. The goal of the proposed system in this research is to extract answers from re-ranked retrieved passages which are retrieved by search engines. This system extracts the answer only to why questions. This system is called by EWAQ: Entailment based Why Arabic Questions Answering. Each answer is scored with entailment metrics and ranked according to their scores in order to determine the most possible correct answer. EWAQ is compared with search engines: yahoo, google and ask.com, the well-established web-based Questions Answering systems, using manual test set. In EWAQ experiments, it is showed that the accuracy is increased by implementing the textual entailment in re-raking the retrieved relevant passages by search engines and deciding the correct answer. The obtained results show that using entailment based similarity can help significantly to tackle the why Answer Extraction module in Arabic language.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Computation & Language
๐
๐
Old Age
๐
๐
Old Age
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
๐
๐
Old Age
XLNet: Generalized Autoregressive Pretraining for Language Understanding
๐ฎ
๐ฎ
The Ethereal
Effective Approaches to Attention-based Neural Machine Translation
๐
๐
Old Age
A large annotated corpus for learning natural language inference
๐
๐
Old Age
HellaSwag: Can a Machine Really Finish Your Sentence?
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
๐ป
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
๐ป
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
๐ป
Ghosted