# Ordinal Common-sense Inference

arXiv: https://arxiv.org/abs/1611.00601 · HTML: https://mlsanity.com/paper/1611.00601/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2016.

## Built on by (most cited first, 30 of 35)

- [We're Afraid Language Models Aren't Modeling Ambiguity](https://mlsanity.com/paper/2304.14399/) (2023)
- [UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark](https://mlsanity.com/paper/2103.13009/) (2021)
- [Variational Information Bottleneck for Effective Low-Resource Fine-Tuning](https://mlsanity.com/paper/2106.05469/) (2021)
- [Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements](https://mlsanity.com/paper/2305.03695/) (2023)
- [COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences](https://mlsanity.com/paper/2106.00969/) (2021)
- [Benchmarks for Automated Commonsense Reasoning: A Survey](https://mlsanity.com/paper/2302.04752/) (2023)
- [Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning](https://mlsanity.com/paper/2104.08676/) (2021)
- [Learning with Different Amounts of Annotation: From Zero to Many Labels](https://mlsanity.com/paper/2109.04408/) (2021)
- [Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks](https://mlsanity.com/paper/2504.18838/) (2025)
- [SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Models](https://mlsanity.com/paper/2210.07269/) (2022)
- [Modeling Event Plausibility with Consistent Conceptual Abstraction](https://mlsanity.com/paper/2104.10247/) (2021)
- [Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding](https://mlsanity.com/paper/2109.04947/) (2021)
- [Refined Commonsense Knowledge from Large-Scale Web Contents](https://mlsanity.com/paper/2112.04596/) (2021)
- [Exploring Transitivity in Neural NLI Models through Veridicality](https://mlsanity.com/paper/2101.10713/) (2021)
- [Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic](https://mlsanity.com/paper/2402.14798/) (2024)
- [Towards Online Multi-Modal Social Interaction Understanding](https://mlsanity.com/paper/2503.19851/) (2025)
- [CLEVRER-Humans: Describing Physical and Causal Events the Human Way](https://mlsanity.com/paper/2310.03635/) (2023)
- [Susu Box or Piggy Bank: Assessing Cultural Commonsense Knowledge between Ghana and the U.S](https://mlsanity.com/paper/2410.16451/) (2024)
- [Entailed Between the Lines: Incorporating Implication into NLI](https://mlsanity.com/paper/2501.07719/) (2025)
- [Visual Grounding Strategies for Text-Only Natural Language Processing](https://mlsanity.com/paper/2103.13942/) (2021)
- [Probing neural language models for understanding of words of estimative probability](https://mlsanity.com/paper/2211.03358/) (2022)
- [Capturing Label Distribution: A Case Study in NLI](https://mlsanity.com/paper/2102.06859/) (2021)
- [No Strong Feelings One Way or Another: Re-operationalizing Neutrality in Natural Language Inference](https://mlsanity.com/paper/2306.09918/) (2023)
- [SpaceNLI: Evaluating the Consistency of Predicting Inferences in Space](https://mlsanity.com/paper/2307.02269/) (2023)
- [Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering](https://mlsanity.com/paper/2105.13662/) (2021)
- [Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios](https://mlsanity.com/paper/2209.07760/) (2022)
- [Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven Optimization](https://mlsanity.com/paper/2412.16232/) (2024)
- [Giving AI Personalities Leads to More Human-Like Reasoning](https://mlsanity.com/paper/2502.14155/) (2025)
- [Autoencoding Language Model Based Ensemble Learning for Commonsense Validation and Explanation](https://mlsanity.com/paper/2204.03324/) (2022)
- [On Degrees of Freedom in Defining and Testing Natural Language Understanding](https://mlsanity.com/paper/2305.15130/) (2023)

## Abstract (arXiv)

Humans have the capacity to draw common-sense inferences from natural language: various things that are likely but not certain to hold based on established discourse, and are rarely stated explicitly. We propose an evaluation of automated common-sense inference based on an extension of recognizing textual entailment: predicting ordinal human responses on the subjective likelihood of an inference holding in a given context. We describe a framework for extracting common-sense knowledge from corpora, which is then used to construct a dataset for this ordinal entailment task. We train a neural sequence-to-sequence model on this dataset, which we use to score and generate possible inferences. Further, we annotate subsets of previously established datasets via our ordinal annotation protocol in order to then analyze the distinctions between these and what we have constructed.