# Sequence to Sequence Learning for Event Prediction

arXiv: https://arxiv.org/abs/1709.06033 · HTML: https://mlsanity.com/paper/1709.06033/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2017.

## Built on by (most cited first, 9 of 9)

- [The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction](https://mlsanity.com/paper/2104.06344/) (2021)
- [Goal-Oriented Script Construction](https://mlsanity.com/paper/2107.13189/) (2021)
- [CaT-BENCH: Benchmarking Language Model Understanding of Causal and Temporal Dependencies in Plans](https://mlsanity.com/paper/2406.15823/) (2024)
- [Reasoning about Procedures with Natural Language Processing: A Tutorial](https://mlsanity.com/paper/2205.07455/) (2022)
- [Benchmarking Procedural Language Understanding for Low-Resource Languages: A Case Study on Turkish](https://mlsanity.com/paper/2309.06698/) (2023)
- [Non-Sequential Graph Script Induction via Multimedia Grounding](https://mlsanity.com/paper/2305.17542/) (2023)
- [PARADISE: Evaluating Implicit Planning Skills of Language Models with Procedural Warnings and Tips Dataset](https://mlsanity.com/paper/2403.03167/) (2024)
- [Understanding in Artificial Intelligence](https://mlsanity.com/paper/2101.06573/) (2021)
- [Automatic Post-Editing for Vietnamese](https://mlsanity.com/paper/2104.12128/) (2021)

## Abstract (arXiv)

This paper presents an approach to the task of predicting an event description from a preceding sentence in a text. Our approach explores sequence-to-sequence learning using a bidirectional multi-layer recurrent neural network. Our approach substantially outperforms previous work in terms of the BLEU score on two datasets derived from WikiHow and DeScript respectively. Since the BLEU score is not easy to interpret as a measure of event prediction, we complement our study with a second evaluation that exploits the rich linguistic annotation of gold paraphrase sets of events.