====== Commonsense Reasoning ======

===== Papers =====

==== General Papers ====

  * [[https://www.aaai.org/Papers/Symposia/Spring/2002/SS-02-09/SS02-09-011.pdf|Singh 2002 - The Public Acquisition of Commonsense Knowledge]] The Open Mind Common Sense project, which was the foundation of ConceptNet
  * [[https://www.aclweb.org/anthology/D14-1059.pdf|Angeli & Manning 2014 - NaturalLI: Natural Logic Inference for Common Sense Reasoning]]
  * [[https://arxiv.org/pdf/1612.03975.pdf|Speer et al 2016 - ConceptNet 5.5: An Open Multilingual Graph of General Knowledge]]
  * [[https://arxiv.org/pdf/1808.05326.pdf|Zellers et al 2018 - SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference]]
    * [[https://arxiv.org/pdf/1905.07830.pdf|Zellers et al 2019 - HellaSwag: Can a Machine Really Finish Your Sentence?]]
  * [[https://arxiv.org/pdf/1811.00146.pdf|Sag et al 2018 - ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning]]
  * [[https://arxiv.org/pdf/1909.00505.pdf|Feldman et al 2019 - Commonsense Knowledge Mining from Pretrained Models]]
  * [[https://arxiv.org/pdf/1906.05317.pdf|Bosselut et al 2019 - COMET: Commonsense Transformers for Automatic Knowledge Graph Construction]] Can be used as a language model-like interface to knowledge contained in ATOMIC or ConceptNet. The “mask” tokens are padding for batch training, see [[https://github.com/atcbosselut/comet-commonsense/issues/31|here]]. **[[https://mosaickg.apps.allenai.org/model_comet2020_people_events|Demo]]**
  * [[https://aclanthology.org/2022.acl-long.225.pdf|Liu et al 2022 - Generated Knowledge Prompting for Commonsense Reasoning]]

==== Commonsense Question Answering ====
  * [[https://arxiv.org/pdf/1811.00937.pdf|Talmor et al 2018 - CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge]]
  * [[https://arxiv.org/pdf/2004.05483.pdf|Shwartz et al 2020 - Unsupervised Commonsense Question Answering with Self-Talk]]

==== Abductive Reasoning ====
  * Papers
    * [[https://arxiv.org/pdf/1908.05739.pdf|Bhagavatula et al 2019 - Abductive Commonsense Reasoning]]



===== Applications ======
  * MT
    * [[https://www.aclweb.org/anthology/2020.findings-emnlp.327.pdf|He et al 2020 - The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation]]

===== Winograd Schema Challenge =====
==== Overviews ====
  * [[https://en.wikipedia.org/wiki/Winograd_schema_challenge|Wikipedia - Winograd Schema Challenge]]
  * [[https://arxiv.org/pdf/2004.13831.pdf|Kocijan et al 2020 - A Review of Winograd Schema Challenge Datasets and Approaches]]

==== Papers ====
  * [[https://oa.upm.es/65046/1/INVE_MEM_2019_323364.pdf|Boguslavsky et al 2019 - A Knowledge-Based Approach to Winograd Schema Challenge]]

===== Datasets =====
A very helpful list of resources is here: [[https://cs.nyu.edu/~davise/Benchmarks/Text.html]]

  * [[https://rowanzellers.com/swag/|SWAG]] 
  * [[https://rowanzellers.com/hellaswag/|HellaSWAG]]
  * Winograd Schema Challenge ([[https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html|original dataset]])
  * [[https://www.tau-nlp.sites.tau.ac.il/commonsenseqa|CommonsenseQA]] [[https://arxiv.org/pdf/1811.00937.pdf|paper]]
  * Abductive Reasoning Datasets
    * ART, $\alpha$NLI and $\alpha$NLG: 20k commonsense narrative contexts and 200k explanations (from ROCStories dataset) [[https://arxiv.org/pdf/1908.05739.pdf|paper]]

===== Demos =====
  * COMET [[https://mosaickg.apps.allenai.org/model_comet2020_people_events|Demo]]

===== Tutorials =====
  * Vared Shwartz at ACl 2020: [[https://homes.cs.washington.edu/~msap/acl2020-commonsense/slides/02%20-%20knowledge%20in%20LMs.pdf|slides]]

===== Related Pages =====
  * [[Question Answering]]