NTUNLPL at F in C ausal 2020, Task 2:Improving Causality Detection Using V iterbi Decoder (2020)

Abstract: In order to provide an explanation of machine learning models, causality detection attracts lots of attention in the artificial intelligence research community. In this paper, we explore the cause-effect detection in financial news and propose an approach, which combines the BIO scheme with the Viterbi decoder for addressing this challenge. Our approach is ranked the first in the official run of cause-effect detection (Task 2) of the FinCausal-2020 shared task. We not only report the implementation details and ablation analysis in this paper, but also publish our code for academic usage.

## introduction adopting causality information as features can benefit lots of applications such as question answering @xcite , event prediction @xcite , and medical text mining @xcite . in the financial domain, causality detection can be applied to stock movement prediction @xcite and supporting financial services @xcite . to better explain the causality that occurs between financial events, cause-effect detection is a fundamental research issue. taking a close look to financial documents, we find that there may exist multiple causal events and multiple causal chains in a paragraph. in such a case, traditional extraction methods like discourse parser are not feasible. in order to deal with this issue, we formulate the cause-effect detection task as a sequence labeling problem and propose an approach using bio scheme and viterbi decoder. we find that the proposed approach has the ability to identify multiple causal events and multiple causal chains in a given short paragraph, and also gives a better event span boundary. our contributions are two-fold as follows. ## pre-processing we experiment on the fincausal-2020 dataset @xcite , which consists of two subtasks, including causal meanings detection (task 1) and cause-effect detection (task 2). the numbers of training instances are 22,058 and 1,750 for task 1 and task 2, respectively. this work only focuses on task 2. we use the stanford corenlp stanza @xcite ) toolkit 2 to tokenize each sentence and generate the part-of-speech (pos) tag for each token. for the examples with multiple causal events, we recognize them by their indices and add a special number token before each example to treat them as different model inputs. as for causal relations tagging, we use "b, i, o" (begin, inside, and outside) and "c, e" (cause and effect) labels to represent the positional information of the words and the semantic roles of the causal events. ## results we perform five-fold cross-validation to verify our experimental results during the self-evaluation period. ## error analysis figure 1 shows an instance for error analysis, where our approach outperforms the baseline models in the self-evaluation experiment. here we use the last partition of the training data as validation set for example. in this case, there are two causal chains. the crf model does not find any causal event. the bert model only succeeds in tagging one of the causal chains, but fails to tag the other effect span "that stake...". in contrast, our approach successfully labels the correct causal events, which shows that it can better identify the proper event span and achieve a higher exact match ratio. figure 2 shows an instance that our best model fails to correctly identify the causal events. in this example, our model could not extract the two correct causal events in one sentence, suggesting that our system still has room for improvement on detecting multiple causal events in a single sentence. ## conclusion this paper presents our approach to causality detection, which is ranked first in the task 2 of fincausal-2020. the ablation analysis shows the effectiveness of the proposed approach. the error analysis also supports that the proposed approach performs better in multiple causal events and multiple causal chains cases. in the future, we plan to adopt the proposed approach to the documents in other domains. we also plan to adopt the extracted causality from financial documents to improve the performance of downstream tasks such as stock movement prediction and financial argument mining.