# 1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position Selector

arXiv: https://arxiv.org/abs/2210.17157 · HTML: https://mlsanity.com/paper/2210.17157/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 16 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Document-Level Event Argument Extraction by Conditional Generation](https://mlsanity.com/paper/2104.05919/) (2021)
- [SciREX: A Challenge Dataset for Document-Level Information Extraction](https://mlsanity.com/paper/2005.00512/) (2020)
- [A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning](https://mlsanity.com/paper/1908.05514/) (2019)
- [Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading](https://mlsanity.com/paper/2105.12825/) (2021)
- [The Causal News Corpus: Annotating Causal Relations in Event Sentences from News](https://mlsanity.com/paper/2204.11714/) (2022)
- [Automated Extraction of Socio-political Events from News (AESPEN): Workshop and Shared Task Report](https://mlsanity.com/paper/2005.06070/) (2020)
- [Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021): Workshop and Shared Task Report](https://mlsanity.com/paper/2108.07865/) (2021)
- [Neural Text Generation: A Practical Guide](https://mlsanity.com/paper/1711.09534/) (2017)

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

- [Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022](https://mlsanity.com/paper/2211.12154/) (2022)
- [BoschAI @ Causal News Corpus 2023: Robust Cause-Effect Span Extraction using Multi-Layer Sequence Tagging and Data Augmentation](https://mlsanity.com/paper/2312.06338/) (2023)
- [Constructing and Interpreting Causal Knowledge Graphs from News](https://mlsanity.com/paper/2305.09359/) (2023)

## Abstract (arXiv)

In this paper, we present our approach and empirical observations for Cause-Effect Signal Span Detection -- Subtask 2 of Shared task 3~citetan-etal-2022-event at CASE 2022. The shared task aims to extract the cause, effect, and signal spans from a given causal sentence. We model the task as a reading comprehension (RC) problem and apply a token-level RC-based span prediction paradigm to the task as the baseline. We explore different training objectives to fine-tune the model, as well as data augmentation (DA) tricks based on the language model (LM) for performance improvement. Additionally, we propose an efficient beam-search post-processing strategy to due with the drawbacks of span detection to obtain a further performance gain. Our approach achieves an average F1 score of 54.15 and ranks textbf1st in the CASE competition. Our code is available at urlhttps://github.com/Gzhang-umich/1CademyTeamOfCASE.