# IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions

arXiv: https://arxiv.org/abs/2312.06053 · HTML: https://mlsanity.com/paper/2312.06053/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 63 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 30 of 30)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://mlsanity.com/paper/2012.15761/) (2020)
- [Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks](https://mlsanity.com/paper/1811.01088/) (2018)
- [CEM: Commonsense-aware Empathetic Response Generation](https://mlsanity.com/paper/2109.05739/) (2021)
- [TransOMCS: From Linguistic Graphs to Commonsense Knowledge](https://mlsanity.com/paper/2005.00206/) (2020)
- [MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding](https://mlsanity.com/paper/2103.06779/) (2021)
- [CSKG: The CommonSense Knowledge Graph](https://mlsanity.com/paper/2012.11490/) (2020)
- [It's not Rocket Science : Interpreting Figurative Language in Narratives](https://mlsanity.com/paper/2109.00087/) (2021)
- [Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Survey](https://mlsanity.com/paper/2201.12438/) (2022)
- [Examining the Tip of the Iceberg: A Data Set for Idiom Translation](https://mlsanity.com/paper/1802.04681/) (2018)
- [Towards an Atlas of Cultural Commonsense for Machine Reasoning](https://mlsanity.com/paper/2009.05664/) (2020)
- [ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities](https://mlsanity.com/paper/2104.02137/) (2021)
- [AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models](https://mlsanity.com/paper/2109.04413/) (2021)
- [Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks](https://mlsanity.com/paper/2105.05457/) (2021)
- [Sentiment Analysis in the News](https://mlsanity.com/paper/1309.6202/) (2013)
- [Investigating Robustness of Dialog Models to Popular Figurative Language Constructs](https://mlsanity.com/paper/2110.00687/) (2021)
- [Idiomatic Expression Identification using Semantic Compatibility](https://mlsanity.com/paper/2110.10064/) (2021)
- [Rethinking Why Intermediate-Task Fine-Tuning Works](https://mlsanity.com/paper/2108.11696/) (2021)
- [Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions](https://mlsanity.com/paper/1802.09961/) (2018)
- [MICE: Mining Idioms with Contextual Embeddings](https://mlsanity.com/paper/2008.05759/) (2020)
- [Idiomatic Expression Paraphrasing without Strong Supervision](https://mlsanity.com/paper/2112.08592/) (2021)
- [Are Sample-Efficient NLP Models More Robust?](https://mlsanity.com/paper/2210.06456/) (2022)

## Built on by (most cited first, 1 of 1)

- [Investigating Idiomaticity in Word Representations](https://mlsanity.com/paper/2411.02610/) (2024)

## Abstract (arXiv)

Idiomatic expression (IE) processing and comprehension have challenged pre-trained language models (PTLMs) because their meanings are non-compositional. Unlike prior works that enable IE comprehension through fine-tuning PTLMs with sentences containing IEs, in this work, we construct IEKG, a commonsense knowledge graph for figurative interpretations of IEs. This extends the established ATOMIC2020 graph, converting PTLMs into knowledge models (KMs) that encode and infer commonsense knowledge related to IE use. Experiments show that various PTLMs can be converted into KMs with IEKG. We verify the quality of IEKG and the ability of the trained KMs with automatic and human evaluation. Through applications in natural language understanding, we show that a PTLM injected with knowledge from IEKG exhibits improved IE comprehension ability and can generalize to IEs unseen during training.