MPII : Multi-Level Mutual Promotion for Inference and Interpretation (2022)

Abstract: In order to better understand the rationale behind model behavior, recent works have exploited providing interpretation to support the inference prediction. However, existing methods tend to provide human-unfriendly interpretation, and are prone to sub-optimal performance due to one-side promotion, i.e. either inference promotion with interpretation or vice versa. In this paper, we propose a multi-level Mutual Promotion mechanism for self-evolved Inference and sentence-level Interpretation (MPII). Specifically, from the model-level, we propose a Step-wise Integration Mechanism to jointly perform and deeply integrate inference and interpretation in an autoregressive manner. From the optimization-level, we propose an Adversarial Fidelity Regularization to improve the fidelity between inference and interpretation with the Adversarial Mutual Information training strategy. Extensive experiments on NLI and CQA tasks reveal that the proposed MPII approach can significantly outperform baseline models for both the inference performance and the interpretation quality.

## introduction recently, the interpretability of neural networks has been of increasing concern. in order to break the black-box of neural networks, many works explore the interpretability of neural networks through providing interpretations to support their inference results @xcite @xcite @xcite . although prior works have made some progress towards interpretable nlp, they tend to provide interpretations that lack human-readability. existing interpretable models usually extract promi-people cannot walk while driving down the street. people walk through a store . passengers a in car driving down the street . token-level explanation: figure 1: comparison of different interpretations: heatmap explanation, alignment rationale, token-level nl explanation, and sentence-level nl explanation. with annotated natural language interpretation datasets available @xcite , methods of generating sentence-level interpretation have been explored recently. @xcite proposed to first generate interpretation and then predict the label only based on the generated interpretation. @xcite proposed to first generate sentence-level interpretations with deep pre-trained language models (such as bert and gpt), then fed those interpretations as extra knowledge to help improve inference performance. we notice that these methods only include one-side promotion: utilizing information contained in interpretation to improve inference, while ignoring the other-side promotion: using inference logic to enhance interpretation. as claimed in @xcite that their one-side promotion improves predictions' faithfulness to generated interpretations, then the otherside should be able to improve interpretation's faithfulness to inference process. this has aroused our thinking: can we deeply fuse these two relevant tasks with ingenious combination skills and achieve mutual promotion for inference and interpretation? in this paper, we propose a multi-level mutual promotion mechanism for self-evolved inference and sentence-level interpretation (mpii). specifically, from the model-level, we propose a stepwise integration mechanism (sim) to iteratively update the inference prediction and generate an interpretation token at each decoding step, and deeply integrate hidden representations of the prediction and the token with two fusion modules. in this way, the model learns to refine the inference conclusion as the interpretation proceeds, and the inference procedure can in turn guide the generation of interpretation at each decoding step. from the optimization-level, we propose an adversarial fidelity regularization (afire) to improve the fidelity between inference and interpretation with the adversarial mutual information (ami) method @xcite , which extends the maximum mutual information optimization objective with the idea of generative adversarial network @xcite . with this training framework, the model is trained against a smart backward network that learns to reward the inference prediction and interpretation of fidelity, which ensures faithfulness and makes the derived interpretation depict the true profile of how the model works @xcite . to verify the effectiveness of mpii, we conduct extensive experiments on two inference tasks: natural language inference (nli) task and commonsense question answering (cqa) task. experiment results reveal that compared with baseline models, our method can achieve mutual promotion on both model inference performance and sentencelevel interpretation quality. meanwhile, through providing simultaneous inference prediction and human-comprehensible interpretation with deep integration mechanism and adversarial training strategy, our model can perform inference and interpretation of fidelity and generate more robust explanations. main contributions of this work include: ## methodology in this section, we introduce stepwise integration mechanism (sim) and adversarial fidelity regularization (afire) in details. utilizing the autoregressive nature of transformer decoder, sim enables deep interaction at every decoding step between inference and interpretation. with the adversarial training strategy, afire enables further integration of latent semantic information between inference and interpretation, and also improves the quality of explanation sentences by bringing them closer to human expressions. ## experiments we intend to verify the mutual promotion effect of sim and afire on the inference ability and interpretablity of model. we choose two tasks requiring inference ability: natural language inference (nli) and commonsense question answering (cqa). ## related work with the great success of natual language inference, many recent works explore the interpretability of neural networks through providing interpretation to support their inference results @xcite @xcite @xcite . three forms of interpretation are provided by these works: (1) feature-based interpretation @xcite @xcite @xcite @xcite such as attention distribution @xcite , heatmap @xcite , alignment rationale @xcite , gradients @xcite , magnitude of hidden states @xcite , etc.; (2) token-level interpretation that relatively easy to comprehend but prone to ambiguity @xcite @xcite , and (3) sentence-level interpretation which has the best human-readability camburu et al. (2018); talmor et al. (2019) ; @xcite . different from the previous work which only include one-side promotion, we proposed the mutual promotion mechanism that can improve the performance of both inference and sentence-level interpretation. ## conclusions in this work, we propose to mutually promote model inference ability and interpretability from multi-levels. from the model-level, we propose stepwise integration mechanism to enable the model to refine the prediction conclusion as the explaining proceeds and also to guide the generation of better explanation with the inference procedure of reaching prediction conclusion. from the optimization-level, we propose an adversarial fidelity regularization, which leverages the adversarial mutual information method to improve the fidelity between the inference and interpretation, which further guarantees faithfulness. experiment results show the effectiveness of our proposed method on both nli and cqa tasks. future work will involve extending our approaches into other tasks of nlp. we hope that our work can encourage further research in this direction.