1 C ademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position Selector (2022)

Abstract: In this paper, we present our approach and empirical observations for Cause-Effect Signal Span Detection—Subtask 2 of Shared task 3 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 F_1 score of 54.15 and ranks in the CASE competition. Our code is available at https://github.com/Gzhang-umich/1CademyTeamOfCASE .

## introduction event extraction has long been a challenging and popular area for natural language processing (nlp) researchers. there are known classic benchmarks, including @xcite and ere @xcite . in recent years, more and more interesting corpora about event detection and extraction have emerged based on different specific source corpora, including biomedical literature @xcite , scientific knowledge resources @xcite , wiki @xcite , and trade-related news @xcite . in sharp contrast, cause-effect signal span detection aims to extract the cause, effect, and signal spans from sentences that have cause-effect relations. cause-effect signal span detection is an innovative and important event detection/extraction task that as-sists in understanding causal relationships from comprehensive sentence samples. as a new corpus with great potential in event extraction challenges, the causal news corpus (cnc) @xcite ) contains socio-political event (spe) text data with annotated causal spans. the cnc event extraction challenge foot_0 is the first cause-effect signal span detection challenge on a social political news corpus. the challenge itself provides a limited number of annotated samples for supervision, making it more difficult compared to other challenging event extraction tasks. the exploration of causality in news data and the detection of corresponding spans is helpful in reading comprehensive language expressions, making cnc attractive to nlp researchers. in this paper, we describe our rc-based model with a carefully designed post-processing strategy. we also conduct ablation studies to analyze the influence of both different training objectives and different hyper-parameter settings of the postprocessing strategy on our model. in addition, we apply an lm-based data augmentation strategy to further better performance gains, given the lowresource challenge. our approach improves performance by a large margin in cause-effect signal span detection compared to any other competitors. the main contributions of our paper are as follow: ## causal news corpus the corpus we used in our model training and evaluation is the cnc dataset @xcite . this dataset is built on the extraction of social-political events from news (aespen) @xcite in 2020 and the case 2021 workshop @ acl-ijcnlp @xcite . each sample in the dataset is annotated with causal labels, that is, whether a sentence contains a causal event. furthermore, some sentences are annotated with the span of the specific cause and effect of a causal event, as well as the signal markers that imply the causality. the spans are labeled @xmath0, <arg1>, @xmath1, effect, and causal signal in the sentence, respectively. note that it is possible to have multiple annotations for the same sentence in the dataset if the sentence contains multiple casual relationships of events. the dataset statistics are shown in ## methodology in this section, we describe in detail the methodology we used in the task. to begin, we introduce the baseline model established from a pretrained language model for the task. next, a beamsearch-based post-processing method is introduced to solve the overlap span detection problem in the baseline model. to address the problem that not all examples have signal markers within the sentence, we propose training a signal classifier to determine whether we need to find the signal span of the target test sample. finally, a pre-trained paraphrasing model is applied for data augmentation. ## experiments in this section, we present the experimental details of training the model and discuss the performance of our proposed approach. ## conclusion this paper introduces a reading comprehensionbased method, an original post-processing strategy, and an lm-based data augmentation trick for the new cause-effect signal span detection competition. we compare the rc-based method with the ner-based one and prove that the rc-based method gets an observing performance gain compared to the ner-based one. we provide experimental results and ablation studies of our beamsearch-based span selector and lm-based data augmentation tricks to analyze their efficiency and prove their compatibility with other tricks. our approach achieves state-of-the-art performance in the new competition.