Common sense reasoning tasks are intended to require the model to go beyond pattern recognition. Instead, the model should use "common sense" or world knowledge to make inferences.
Event2Mind is a crowdsourced corpus of 25,000 event phrases covering a diverse range of everyday events and situations. Given an event described in a short free-form text, a model should reason about the likely intents and reactions of the event's participants. Models are evaluated based on average cross-entropy (lower is better).
| Model | Dev | Test | Paper / Source | Code |
|---|---|---|---|---|
| BiRNN 100d (Rashkin et al., 2018) | 4.25 | 4.22 | Event2Mind: Commonsense Inference on Events, Intents, and Reactions | |
| ConvNet (Rashkin et al., 2018) | 4.44 | 4.40 | Event2Mind: Commonsense Inference on Events, Intents, and Reactions |
Situations with Adversarial Generations (SWAG) is a dataset consisting of 113k multiple choice questions about a rich spectrum of grounded situations.
| Model | Dev | Test | Paper / Source | Code |
|---|---|---|---|---|
| BERT Large (Devlin et al., 2018) | 86.6 | 86.3 | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | |
| BERT Base (Devlin et al., 2018) | 81.6 | - | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | |
| ESIM + ELMo (Zellers et al., 2018) | 59.1 | 59.2 | SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference | |
| ESIM + GloVe (Zellers et al., 2018) | 51.9 | 52.7 | SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference |
The Winograd Schema Challenge is a dataset for common sense reasoning. It employs Winograd Schema questions that require the resolution of anaphora: the system must identify the antecedent of an ambiguous pronoun in a statement. Models are evaluated based on accuracy.
Example:
The trophy doesn’t fit in the suitcase because it is too big. What is too big? Answer 0: the trophy. Answer 1: the suitcase
| Model | Score | Paper / Source |
|---|---|---|
| Word-LM-partial (Trinh and Le, 2018) | 62.6 | A Simple Method for Commonsense Reasoning |
| Char-LM-partial (Trinh and Le, 2018) | 57.9 | A Simple Method for Commonsense Reasoning |
| USSM + Supervised DeepNet + KB (Liu et al., 2017) | 52.8 | Combing Context and Commonsense Knowledge Through Neural Networks for Solving Winograd Schema Problems |