Awesome Question Answering

URL: https://github.com/seriousran/awesome-qa#readme
Category: Themed Directories
Tags: nlp, machine-learning, datasets
Brand: awesome-lists

Overview

Awesome Question Answering is a curated, open-source directory of resources related to automated question answering (QA) in natural language. It focuses on QA as a subfield of information retrieval and natural language processing (NLP), emphasizing machine learning and deep learning approaches. The content is presented in multiple languages (English, Korean, Chinese).

Features

• Curated QA Resource List

  • Organized collection of links and references centered on Question Answering systems and research.
  • Covers QA as a discipline in information retrieval and NLP.
  • Emphasis on machine learning and deep learning–based QA methods.

• Structured Contents Section

  • Table-of-contents style navigation to different QA-related topics.
  • Sections for trends, models, datasets, systems, and competitions.

• Recent Trends in QA

  • Recent QA Models: Highlights or catalogs newly proposed QA architectures and approaches.
  • Recent Language Models: Focus on language models relevant to QA (e.g., large language models and other NLP backbones).
  • Subsections for major conferences and publication venues:
    • AAAI 2020
    • ACL 2019
    • EMNLP-IJCNLP 2019
    • Arxiv (recent QA-related preprints and papers)
  • Dataset subsection under trends, indicating tracked or listed QA datasets.

• About QA

  • Conceptual overview of what Question Answering is.
  • Types of QA: High-level taxonomy of different QA task types.
  • Subtypes of QA: Further breakdown of QA variants (e.g., likely covering reading comprehension, open-domain QA, etc., though not explicitly listed in the snippet).
  • Analysis and Parsing for Pre-processing in QA Systems:
    • Discussion of language analysis required for QA pipelines.
  • Typical QA System Architecture:
    • Description of how most QA systems have roughly three parts (e.g., likely information retrieval, reasoning/understanding, and answer generation or selection).

• Events

  • Section dedicated to QA-related events (e.g., workshops, shared tasks, or conferences). Specific items are not shown in the snippet but the structure indicates listings.

• Systems

  • Section presumably listing implemented QA systems, frameworks, or reference implementations.

• Competitions in QA

  • Tabular overview of QA benchmarks and competitions, including:
    • Dataset name and link (e.g., Story Cloze Test, MS MARCO, MS MARCO V2, SQuAD, SQuAD 2.0).
    • Language (e.g., English).
    • Organizer (e.g., Univ. of Rochester, Microsoft, Univ. of Stanford).
    • Since (year started, e.g., 2016, 2018).
    • Top Rank / Leading Team (e.g., msap, YUANFUDAO research NLP, XLNet Team, PINGAN Omni-Sinitic).
    • Model (e.g., Logistic regression, MARS, Masque Q&A Style, XLNet single model, ALBERT + DAAF + Verifier ensemble).
    • Status (e.g., Closed, Opened).
    • Over Human Performance indicator (e.g., o/x denoting whether systems surpass human performance on that benchmark).

• Repository Structure & Community Files

  • README.md with the main awesome list content and structure.
  • LICENSE file specifying usage and distribution terms.
  • CODE_OF_CONDUCT.md outlining community behavior expectations for contributors.
  • contributing.md describing how to propose additions or changes to the list.
  • images directory for visual assets used in the documentation.
  • mds directory, likely containing additional markdown files for sub-pages or sections.

• Multilingual Introductory Descriptions

  • Brief descriptions of the QA domain and the purpose of the list in:
    • English
    • Korean
    • Chinese

Use Cases

• Discovering QA datasets, benchmarks, and competitions for research or development.
• Finding recent QA and language models relevant to building QA systems.
• Understanding the taxonomy and architecture of QA systems.
• Exploring events and systems in the QA ecosystem.
• Using as a reference hub when starting or expanding QA-related research or projects.

Licensing

• The repository includes a LICENSE file specifying the license for the awesome list contents (exact license text is in the repo’s LICENSE file).

Pricing

• Not applicable. This is an open-source, curated directory (awesome list) with no pricing plans indicated.