# MultiMUC: Multilingual Template Filling on MUC-4

arXiv: https://arxiv.org/abs/2401.16209 · HTML: https://mlsanity.com/paper/2401.16209/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 74 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## 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)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning](https://mlsanity.com/paper/2304.05613/) (2023)
- [Word Alignment by Fine-tuning Embeddings on Parallel Corpora](https://mlsanity.com/paper/2101.08231/) (2021)
- [Towards a Cleaner Document-Oriented Multilingual Crawled Corpus](https://mlsanity.com/paper/2201.06642/) (2022)
- [Towards Making the Most of ChatGPT for Machine Translation](https://mlsanity.com/paper/2303.13780/) (2023)
- [Exploring the Feasibility of ChatGPT for Event Extraction](https://mlsanity.com/paper/2303.03836/) (2023)
- [Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus](https://mlsanity.com/paper/2004.06295/) (2020)
- [Document-level Entity-based Extraction as Template Generation](https://mlsanity.com/paper/2109.04901/) (2021)
- [Frustratingly Easy Label Projection for Cross-lingual Transfer](https://mlsanity.com/paper/2211.15613/) (2022)
- [LOME: Large Ontology Multilingual Extraction](https://mlsanity.com/paper/2101.12175/) (2021)
- [Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction](https://mlsanity.com/paper/2109.06798/) (2021)
- [X-SRL: A Parallel Cross-Lingual Semantic Role Labeling Dataset](https://mlsanity.com/paper/2010.01998/) (2020)
- [A Discriminative Neural Model for Cross-Lingual Word Alignment](https://mlsanity.com/paper/1909.00444/) (2019)
- [Language Model Priming for Cross-Lingual Event Extraction](https://mlsanity.com/paper/2109.12383/) (2021)
- [Probabilistic Frame Induction](https://mlsanity.com/paper/1302.4813/) (2013)
- [Iterative Document-level Information Extraction via Imitation Learning](https://mlsanity.com/paper/2210.06600/) (2022)
- [A Unified View of Evaluation Metrics for Structured Prediction](https://mlsanity.com/paper/2310.13793/) (2023)
- [Automatic Error Analysis for Document-level Information Extraction](https://mlsanity.com/paper/2209.07442/) (2022)
- [Cross-Lingual Transfer of Semantic Roles: From Raw Text to Semantic Roles](https://mlsanity.com/paper/1904.03256/) (2019)
- [Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic Capacity](https://mlsanity.com/paper/2305.02176/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Survey of Video Datasets for Grounded Event Understanding](https://mlsanity.com/paper/2406.09646/) (2024)
- [Event-Keyed Summarization](https://mlsanity.com/paper/2402.06973/) (2024)
- [LEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real World](https://mlsanity.com/paper/2506.00980/) (2025)

## Abstract (arXiv)

We introduce MultiMUC, the first multilingual parallel corpus for template filling, comprising translations of the classic MUC-4 template filling benchmark into five languages: Arabic, Chinese, Farsi, Korean, and Russian. We obtain automatic translations from a strong multilingual machine translation system and manually project the original English annotations into each target language. For all languages, we also provide human translations for sentences in the dev and test splits that contain annotated template arguments. Finally, we present baselines on MultiMUC both with state-of-the-art template filling models and with ChatGPT.