# Self-supervised Knowledge Triplet Learning for Zero-shot Question Answering

arXiv: https://arxiv.org/abs/2005.00316 · HTML: https://mlsanity.com/paper/2005.00316/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

## Built on by (most cited first, 29 of 29)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Prompting Contrastive Explanations for Commonsense Reasoning Tasks](https://mlsanity.com/paper/2106.06823/) (2021)
- [Knowledge-aware Zero-Shot Learning: Survey and Perspective](https://mlsanity.com/paper/2103.00070/) (2021)
- [Dimensions of Commonsense Knowledge](https://mlsanity.com/paper/2101.04640/) (2021)
- [Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2112.10006/) (2021)
- [The Life Cycle of Knowledge in Big Language Models: A Survey](https://mlsanity.com/paper/2303.07616/) (2023)
- [Self-Supervised Test-Time Learning for Reading Comprehension](https://mlsanity.com/paper/2103.11263/) (2021)
- [Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination](https://mlsanity.com/paper/2210.12261/) (2022)
- [Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization](https://mlsanity.com/paper/2206.01532/) (2022)
- [Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2](https://mlsanity.com/paper/2103.13033/) (2021)
- [Abductive Commonsense Reasoning Exploiting Mutually Exclusive Explanations](https://mlsanity.com/paper/2305.14618/) (2023)
- [A Semantic-based Method for Unsupervised Commonsense Question Answering](https://mlsanity.com/paper/2105.14781/) (2021)
- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (2021)
- [Distilling Hypernymy Relations from Language Models: On the Effectiveness of Zero-Shot Taxonomy Induction](https://mlsanity.com/paper/2202.04876/) (2022)
- [DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models](https://mlsanity.com/paper/2110.01509/) (2021)
- [Unsupervised Natural Language Inference Using PHL Triplet Generation](https://mlsanity.com/paper/2110.08438/) (2021)
- [MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation](https://mlsanity.com/paper/2210.07570/) (2022)
- [Multi-hop Commonsense Knowledge Injection Framework for Zero-Shot Commonsense Question Answering](https://mlsanity.com/paper/2305.05936/) (2023)
- [Automatic Detection of Entity-Manipulated Text using Factual Knowledge](https://mlsanity.com/paper/2203.10343/) (2022)
- [Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning](https://mlsanity.com/paper/2206.03715/) (2022)
- [An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs](https://mlsanity.com/paper/2205.10661/) (2022)
- [Encoding Explanatory Knowledge for Zero-shot Science Question Answering](https://mlsanity.com/paper/2105.05737/) (2021)
- [Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision](https://mlsanity.com/paper/2107.08582/) (2021)
- [Generalizable Neuro-symbolic Systems for Commonsense Question Answering](https://mlsanity.com/paper/2201.06230/) (2022)
- [BUCA: A Binary Classification Approach to Unsupervised Commonsense Question Answering](https://mlsanity.com/paper/2305.15932/) (2023)
- [Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction](https://mlsanity.com/paper/2105.12392/) (2021)
- [Transfer Learning for Multi-lingual Tasks -- a Survey](https://mlsanity.com/paper/2110.02052/) (2021)
- [ActKnow: Active External Knowledge Infusion Learning for Question Answering in Low Data Regime](https://mlsanity.com/paper/2112.09423/) (2021)
- [Unsupervised multiple choices question answering via universal corpus](https://mlsanity.com/paper/2402.17333/) (2024)

## Abstract (arXiv)

The aim of all Question Answering (QA) systems is to be able to generalize to unseen questions. Current supervised methods are reliant on expensive data annotation. Moreover, such annotations can introduce unintended annotator bias which makes systems focus more on the bias than the actual task. In this work, we propose Knowledge Triplet Learning (KTL), a self-supervised task over knowledge graphs. We propose heuristics to create synthetic graphs for commonsense and scientific knowledge. We propose methods of how to use KTL to perform zero-shot QA and our experiments show considerable improvements over large pre-trained transformer models.