Question answering is a subfield of information retrieval. It is a task of answering a question posted in a natural language. A question answering system (QAS) may be considered a good alternative to search engines that return a set of related documents. The QAS system is composed of three main modules; question analysis, passage retrieval, and answer extraction. Over the years, numerous QASs have been presented for use in different languages. However, the the development of Arabic QASs has been slowed by linguistic challenges and the lack of resources and tools available to researchers. In this survey, we start with the challenges due to the language and how these challenges make the development of new Arabic QAS more difficult. Next, we do a detailed review of several Arabic QASs. This is followed by an in-depth analysis of the techniques and approaches in the three modules of a QAS. We present an overview of important and recent tools that were developed to help the researchers in this field. We also cover the available Arabic and multilingual datasets, and a look at the different measures used to assess QASs. Finally, the survey delves into the future direction of Arabic QAS systems based on the current state-of-the-art techniques developed for question answering in other languages.

Arabic question answering system: a survey

Conversational AI is one of the most active research areas in AI, and it has gained more attention from academia as well as industry. Given recent advancements in several conversational AI systems in addition to the availability of several datasets, the aim of this study is to explore the landscape of Arabic text-based conversational AI systems. In this work, we provide a thorough review of recent Arabic conversational AI systems. We group them into three categories based on their functionality: (1) question-answering (QA) systems, (2) task-oriented dialogue systems (DS), and (3) chatbots. Furthermore, we describe the common datasets used in building and evaluating conversational AI systems in Arabic. Few surveys have targeted the conversational AI field for the Arabic language, and we aim to cover this gap with this study. Our contribution focuses on reviewing and analyzing the literature in the field and highlighting future research directions towards human-like conversational AI systems in Arabic.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09723043.pdf

Recent Developments in Arabic Conversational AI: A Literature Review

Conversational AI is one of the most active research areas in AI, and it has gained more attention from academia as well as industry. Given recent advancements in several conversational AI systems in addition to the availability of several datasets, the aim of this study is to explore the landscape of Arabic text-based conversational AI systems. In this work, we provide a thorough review of recent Arabic conversational AI systems. We group them into three categories based on their functionality: (1) question-answering (QA) systems, (2) task-oriented dialogue systems (DS), and (3) chatbots. Furthermore, we describe the common datasets used in building and evaluating conversational AI systems in Arabic. Few surveys have targeted the conversational AI field for the Arabic language, and we aim to cover this gap with this study. Our contribution focuses on reviewing and analyzing the literature in the field and highlighting future research directions towards human-like conversational AI systems in Arabic. 

/pdf/recent-developments-in-arabic-conversational-ai-a-literature-2sjtr4ki.pdf

Arabic factoid Question-Answering system for Islamic sciences using normalized corpora.

Knowledge graphs (KGs) are widely used to store and access information about entities and their relationships. Given a query, the task of entity retrieval from a KG aims at presenting a ranked list of entities relevant to the query. Lately, an increasing number of models for entity retrieval have shown a significant improvement over traditional methods. These models, however, were developed for English KGs. In this work, we build on one such system, named KEWER, to propose SERAG (Semantic Entity Retrieval from Arabic knowledge Graphs). Like KEWER, SERAG uses random walks to generate entity embeddings. DBpedia-Entity v2 is considered the standard test collection for entity retrieval. We discuss the challenges of using it for non-English languages in general and Arabic in particular. We provide an Arabic version of this standard collection, and use it to evaluate SERAG. SERAG is shown to significantly outperform the popular BM25 model thanks to its multi-hop reasoning.

SERAG: Semantic Entity Retrieval from Arabic Knowledge Graphs

The enormous increase of the amount of information available on the web creates the need for systems like Question Answering to bridge the gap between general end users and the web with its different data representations. A considerable portion of the available data on the web is written in Arabic for and by Arabic users. This paper provides a review of the Arabic Question Answering Systems building processes and the challenges met by the researchers in this topic due to the Arabic language special characteristics. A general architecture is represented for the Question Answering task on both structured and unstructured data. Then, an overview of the work done in Arabic Question Answering Systems is presented. Finally, a number of tools and linguistic resources are recommended for researchers to develop Arabic question answering systems. General Terms Question Answering, Question Answering Systems, Natural Language Processing, Information Retrieval

/pdf/arabic-question-answering-a-study-on-challenges-systems-and-2qconigcqy.pdf

Heba Samy

Papers

Arabic Question Answering: A Study on Challenges, Systems, and Techniques