IIT kgp at F in C ausal 2020, Shared Task 1: Causality Detection using Sentence Embeddings in Financial Reports (2020)

Abstract: The paper describes the work that the team submitted to FinCausal 2020 Shared Task. This work is associated with the first sub-task of identifying causality in sentences. The various models used in the experiments tried to obtain a latent space representation for each of the sentences. Linear regression was performed on these representations to classify whether the sentence is causal or not. The experiments have shown BERT (Large) performed the best, giving a F1 score of 0.958, in the task of detecting the causality of sentences in financial texts and reports. The class imbalance was dealt with a modified loss function to give a better metric score for the evaluation.

## introduction the last few decades have seen an advent in the storage capacities which has inherently increased the amount of data that people can store. this generates a massive amount of data as well. one such type of data which is of utmost importance is financial data comprising mainly of financial reports. it is not possible to go through all the reports as the number of such reports keep on increasing with time and it is expensive to hire people to summarize the main contents. but these data can contain important information which might help in explaining the profits gained or losses incurred by a branch or a company as a whole. it also provides key insights to the company's decisions and it's monetary effect on the stock prices of the company. thus there is also a need for more and more financial analysis. in order to explain the variability in the data and to draw conclusions from it, we need to identify the causal relationships between various sentences occurring in the document. a causal relationship implies that there is an underlying dependency between the two clauses of the sentence. if cause clause triggers the effect clause in the sentence and the effect can be explained by the cause, this implies that sentence is causal. the trigger word that can be used to determine and separate the two clauses can be implicit like verbs, propositions, conjunctions etc. or can be explicit that can be understood from the overall structure of the sentence. but the triggers can be present in multiple positions and all of them might not contribute to the main causal relationship present in the sentence. also, the presence of these triggers does not always imply that the sentence has causality. thus it is important to improve from regular expression based methods and introduce a new method. causality has already been studied extensively in a general field but it has not been extended to the field of financial data considerably. the fincausal 2020 tries to extend the same methodologies in the financial domain and see their performance to have a more comprehensive understanding of what works better in the domain of finance. the challenges posed by financial narratives are at the heart of a lot of discussions within the finance industry. as a natural language generation provider with a specialized focus on financial reporting, yseop (d., mariko and h., abi akl and e., labidurie and s., durfort and h. de, mazancourt, and m., el-haj, 2019) is often faced with the need to deliver a relevant mapping of events, indicators, and facts, making causality one of their main research topics. the task we are tackling here can be considered analogous to a sequence classification task where we are required to assign labels to a sentence. this motivated us to use a transformer-based architecture which has shown state of the art results in many natural language tasks like text classification, language modelling, neural machine translation, etc. in this shared task, the phenomenon of lexical causative @xcite is not taken into account. a lexical causative is a causal relationship stated through specific connectives (generally predicates) which, from a semantic point of view, also bear the effect of the cause. we will not consider those as causal references, since the effects are implied in the connectives' definition. for instance in "the company decreased its provisions in 2018.", decrease is a lexical causative that can be glossed as make something lower. there can also be different types of relationships along with the causal relationship in the identified text section. it is often rendered with the use of polysemous connectives whose main function is not to introduce a causal relationship. for example, in this sentence: "zhao found himself 60 million yuan indebted after losing 9,000 btc in a single day (february 10, 2014)", the main function of the connective after is to express a temporal relation between the two clauses. but we also have a causal relationship between them, since one triggers the other. we are using the term text section since it could be a phrase, a sentence or a paragraph in which the cause and the effect are split in different sentences. ## data the data was provided as a part of the financial narrative workshop by the organisers @xcite . the data was mainly provided as csv files. the training data consisted of about 4200 sentences. the data was divided into two classes depending on whether they had causality in their underlying structure (causal sentence) or not (non-causal sentence). the data mainly consisted of non-causal sentences and the number of non-causal sentences was about 16 times that of causal sentences. ## discussion this method uses contextual word embeddings and removes most of the problems a regular expression based method would have had. based on the results, a comparative study was done on the performance of various models like bert, xlnet and roberta. the bert-large model performs better than bert-base as expected as it can have more hidden features to explore. xlnet performs better than bert as the former is a mix of both masked and permutation language modeling. roberta performed worse than bert despite it being trained on more data. one of the reasons for that might be as roberta is not trained on next sentence prediction (nsp). better results are expected on cased versions of language models as compared to the uncased versions. uncased versions turn all the sentences to lowercase which might lead to loss of information in some cases. the added weights on the loss function also helped to increase the recall and thereby helped to tackle the class imbalance. ## conclusion we had experimented with various models and compared the relative accuracy on the given dataset. we finally submitted the best results on a given part of the dataset due to computation limitations and obtained an f1 score 0f 0.958. we can conclude that the cased models performed better than uncased models in all scenarios that we encountered.