# Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation

arXiv: https://arxiv.org/abs/2210.12902 · HTML: https://mlsanity.com/paper/2210.12902/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 32 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 27 of 27)

- [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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [UnifiedQA: Crossing Format Boundaries With a Single QA System](https://mlsanity.com/paper/2005.00700/) (2020)
- [NewsQA: A Machine Comprehension Dataset](https://mlsanity.com/paper/1611.09830/) (2016)
- [Probing Neural Network Comprehension of Natural Language Arguments](https://mlsanity.com/paper/1907.07355/) (2019)
- [CLEAR: Contrastive Learning for Sentence Representation](https://mlsanity.com/paper/2012.15466/) (2020)
- [CERT: Contrastive Self-supervised Learning for Language Understanding](https://mlsanity.com/paper/2005.12766/) (2020)
- [COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining](https://mlsanity.com/paper/2102.08473/) (2021)
- [Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation](https://mlsanity.com/paper/2104.08678/) (2021)
- [Logic-Guided Data Augmentation and Regularization for Consistent Question Answering](https://mlsanity.com/paper/2004.10157/) (2020)
- [A Multi-Axis Annotation Scheme for Event Temporal Relations](https://mlsanity.com/paper/1804.07828/) (2018)
- [ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning](https://mlsanity.com/paper/2012.15022/) (2020)
- [TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions](https://mlsanity.com/paper/2005.00242/) (2020)
- [ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning](https://mlsanity.com/paper/2104.08350/) (2021)
- [Event-QA: A Dataset for Event-Centric Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2004.11861/) (2020)
- [EventKG - the Hub of Event Knowledge on the Web - and Biographical Timeline Generation](https://mlsanity.com/paper/1905.08794/) (2019)
- [Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies](https://mlsanity.com/paper/2102.08366/) (2021)
- [Extracting Event Temporal Relations via Hyperbolic Geometry](https://mlsanity.com/paper/2109.05527/) (2021)
- [A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews](https://mlsanity.com/paper/2010.11384/) (2020)
- [From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension](https://mlsanity.com/paper/2112.04735/) (2021)
- [Learning with Instance Bundles for Reading Comprehension](https://mlsanity.com/paper/2104.08735/) (2021)
- [CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering](https://mlsanity.com/paper/2011.00519/) (2020)

## Built on by (most cited first, 5 of 5)

- [NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization](https://mlsanity.com/paper/2302.05574/) (2023)
- [Event Temporal Relation Extraction with Bayesian Translational Model](https://mlsanity.com/paper/2302.04985/) (2023)
- [Cascading Large Language Models for Salient Event Graph Generation](https://mlsanity.com/paper/2406.18449/) (2024)
- [Explaining Matters: Leveraging Definitions and Semantic Expansion for Sexism Detection](https://mlsanity.com/paper/2506.06238/) (2025)
- [Event Knowledge Incorporation with Posterior Regularization for Event-Centric Question Answering](https://mlsanity.com/paper/2305.04522/) (2023)

## Abstract (arXiv)

Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA). To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR. Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines. The transformation matrix is fine-tuned with the annotated event relation types between events that occurred in questions and those in answers, using event-aware question vectors. Experimental results on the Event Semantic Relation Reasoning (ESTER) dataset show significant improvements in both generative and extractive settings compared to the existing strong baselines, achieving over 8.4% gain in the token-level F1 score and 3.0% gain in Exact Match (EM) score under the multi-answer setting. Qualitative analysis reveals the high quality of the generated answers by TranCLR, demonstrating the feasibility of injecting event knowledge into QA model learning. Our code and models can be found at https://github.com/LuJunru/TranCLR.