Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification

Proceedings of the 28th International Conference on Computational Linguistics, pages 6150–6160 Barcelona, Spain (Online), December 8-13, 2020 6150 Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification Linyi Yang1, Eoin M. Kenny1, Tin Lok Ng2, Yi Yang3, Barry Smyth1, and Ruihai Dong1 1Insight Centre ,University College Dublin, Ireland 1{first.last}@insight-centre.org 2University of Wollongong, Australia 2jamesng@uow.edu.au 3The Hong Kong University of Science and Technology, Hong Kong, China 3imyiyang@ust.hk Abstract Corporate mergers and acquisitions (M&A) account for billions of dollars of investment globally every year, and offer an interesting and challenging domain for artificial intelligence. However, in these highly sensitive domains, it is crucial to not only have a highly robust and accurate model, but be able to generate useful explanations to garner a user’s trust in the automated sys- tem. Regrettably, the recent research regarding eXplainable AI (XAI) in financial text classifi- cation has received little to no attention, and many current methods for generating textual-based explanations result in highly implausible explanations, which damage a user’s trust in the sys- tem. To address these issues, this paper proposes a novel methodology for producing plausible counterfactual explanations, whilst exploring the regularization benefits of adversarial training on language models in the domain of FinTech. Exhaustive quantitative experiments demonstrate that not only does this approach improve the model accuracy when compared to the current state- of-the-art and human performance, but it also generates counterfactual explanations which are significantly more plausible based on human trials. 1 Introduction and Related Work In recent years, large-scale, pre-trained transformer models have led to massive improvements on a wide range of natural language processing (NLP) tasks (Devlin et al., 2018; Liu et al., 2019), including finan- cial technology applications (Duan et al., 2018; Yang et al., 2018; Xing et al., 2019; Yang et al., 2020). However, this impressive ability also coincides with an inherent lack of robustness and transparency, which undermines human trust in the prediction outcome. In the highly sensitive (and financially lu- crative) area of FinTech, explainable financial text classification remains an open, and highly alluring question. To tackle this problem, this paper advances a novel approach which first applies robust trans- former models (by leveraging adversarial training) on a real-world, up-to-date, self-collected mergers and acquisitions (M&A) dataset, and then generating plausible, post-hoc, counterfactual explanations. In the remainder of this section, we describe relevant work to both of these areas before detailing our contributions. 1.1 Artificial Intelligence in Mergers and Acquisitions M&As have reshaped the global business landscape for generations, and are having an accelerating impact on the world’s economy as new technologies such as the internet, big data, and artificial intel- ligence disrupt many business sectors (Yan et al., 2016). To appreciate this, a recent economic study provided strong evidence that M&A deal rumours could influence the share price volatility of rumor target firms (Ma and Zhang, 2016). In particular, they showed that, on average, M&A rumors have a positive short term impact and a negative long term impact on the cumulative abnormal returns of …

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