SNU -Causality Lab @ Causal News Corpus 2022: Detecting Causality by Data Augmentation via Part-of-Speech tagging (2022)

Abstract: Finding causal relations in texts has been a challenge since it requires methods ranging from defining event ontologies to developing proper algorithmic approaches. In this paper, we developed a framework which classifies whether a given sentence contains a causal event. As our approach, we exploited an external corpus that has causal labels to overcome the small size of the original corpus (Causal News Corpus) provided by task organizers. Further, we employed a data augmentation technique utilizing Part-Of-Speech (POS) based on our observation that some parts of speech are more (or less) relevant to causality. Our approach especially improved the recall of detecting causal events in sentences.

## introduction nowadays, unprecedented amounts of data on social, political, and economic events offer a breakthrough potential for data-driven analytics. it drives and helps informed policy-making in the social and human sciences. data of those humanities and social sciences cover a broad range of materials from structured numerical datasets to unstructured text data. an event is a specific occurrence of something that happens in a certain time and place involving humans. the events in texts can be understood in terms of causality, implies when one event, process, state, or object (namely, "cause") contributes to the production of another one (namely, "effect") where the cause is responsible for the effect. event-relating studies in the nlp have been growing, such as event extraction (ee), name entity recognition (ner), and relation extraction @xcite . in particular, ee requires identifying the event, classifying event type and argument, and judging the argument role to collect knowledge about incidents found in texts @xcite . recent approaches to ee have taken advantage of dense features extractions by neural network models @xcite @xcite as well as contextualized lexical representations from pre-trained language models @xcite . however, there exist few studies regarding identifying or classifying events, especially based on causal relations. phu and nguyen (2021) studied event causality identification (eci) based on graph convolutional networks to learn document context-augmented representations for causality prediction between events. @xcite developed a model to learn a structure for event causality reasoning. moreover, man et al. ( 2022 ) introduced dependency path generation as a complementary task for eci using causal label prediction. in this study, we focus on causal event classification: whether a sentence contains any causal relation. our framework employed both recent and traditional nlp techniques, which are pre-trained large language model (i.e., electra @xcite ) and pos tagging @xcite . to enhance the performance of detecting causality in each sentence, we attempted not only to concatenate another corpus that has causal labels but also to augment those corpora via pos tagging. with our base model, electra, those different combinations of datasets were compared to one another. this paper is organized as follows. we first explore and examine the task and datasets. based on the examination, we propose a new method in section 3. we then present experimental results and discussion. the offered dataset is 'causal news corpus (cnc)' @xcite . cnc contains sentences randomly sampled and refined from socio-political news. each sentence in the dataset has a label, which represents whether it has a cause-effect relationship. this dataset was successfully used in automated extraction of socio-political events from news (aespen) at language resource and evaluation conference (lrec) in 2020 @xcite and challenges and applications of automated extraction of socio-political events from text (case) in 2021 @xcite . the number of training and validation data are 2925 and 323, respectively. additionally, the organizers prepared the test set (which is only accessible through the task evaluation system) of size 311. we additionally utilized an external dataset, 'semeval-2010,' which was created for semeval-2010 task 8 @xcite . the task was to classify semantic relations between pairs of nominals. one of the semantic relations is a causal relationship. hence, we can directly infer whether a sentence in the dataset contains a causal relationship or not, allowing us to create another dataset to classify causality. "the complication arose from the light irradiation." is an example of a cause-effect labeled sentence from semeval-2010. the training and test (used as validation) datasets contain 4450 and 786 sentences, respectively. ## results & discussion the performances of different datasets are compared ( ## conclusion in this work, we proposed a framework that detects causal events from a sentence. in particular, because of the scarce number of sentences having causal relations, we devised a data augmentation strategy utilizing pos tags in place of causally irrelevant words. by augmenting the datasets, we indirectly increased the impact of verbs or conjunctions since causality relies on specific partsof-speech in the context rather than the semantic meaning. the data augmentation strategy enhanced the performance of detecting causality especially in terms of recall and f 1 . given that the number of sentences having causal relations is small, detecting causality in those sentences is considered much more valuable than one in non-causal sentences. our contribution is that we provided an unconventional way of exploiting pos tags: previous studies using data augmentation via pos tagging enhanced the impact of specific words, such as informing proper nouns and word order for translation @xcite . in contrast, we weaken the impact of specific words to indirectly improve the impact of other important words for detecting causality in sentences, such as verbs and conjunctions. by replacing those superfluous words with corresponding tags and adding those newly created sentences into the original corpus, our model outperformed those without data augmentation. this method can be a proper choice when adding new datasets is too expensive or there are few labeled datasets available.