Constructing Word-Context-Coupled Space Aligned with Associative Knowledge Relations for Interpretable Language Modeling (2023)

Abstract: As the foundation of current natural language processing methods, pre-trained language model has achieved excellent performance. However, the black-box structure of the deep neural network in pre-trained language models seriously limits the interpretability of the language modeling process. After revisiting the coupled requirement of deep neural representation and semantics logic of language modeling, a Word-Context-Coupled Space (W2CSpace) is proposed by introducing the alignment processing between uninterpretable neural representation and interpretable statistical logic. Moreover, a clustering process is also designed to connect the word- and context-level semantics. Specifically, an associative knowledge network (AKN), considered interpretable statistical logic, is introduced in the alignment process for word-level semantics. Furthermore, the context-relative distance is employed as the semantic feature for the downstream classifier, which is greatly different from the current uninterpretable semantic representations of pre-trained models. Our experiments for performance evaluation and interpretable analysis are executed on several types of datasets, including SIGHAN, Weibo, and ChnSenti. Wherein a novel evaluation strategy for the interpretability of machine learning models is first proposed. According to the experimental results, our language model can achieve better performance and highly credible interpretable ability compared to related state-of-the-art methods.

## introduction machine learning has recently been democratized in various domains, such as search engines, conversational systems, and autonomous driving @xcite @xcite . however, despite ai technologies signifi-cantly facilitating industrial processes and improving work experiences, the uninterpretable logic of machines leads to distrust, which hinders further development of ai. explainable artificial intelligence (xai), proposed to bridge the block between humans and machines, has been increasingly attracting attention recently, where "explanation" is described as abductive inference and transferring knowledge @xcite . calling for explaining and understanding the machine learning process, researchers aim to interpret the methods for system verification, compliance with legislation, and technology improvement. computational linguistics, which serves as the theoretical foundation for nlp, aims to promote communication between humans and machines @xcite . however, during the recent development, the uninterpretable nlp methods have raised concerns. the decreased transparency and increased parameter complexity adversely affect the model explainability and controllability, even if the performance of the language models has significantly improved, such as berts @xcite @xcite , gpts @xcite @xcite ) and so on. the performance advantage of the black-box methods is attractive while the researchers investigate interpretable algorithms. therefore, the existing works mainly focus on (1) explaining the blackbox methods and (2) using interpretable models @xcite . in order to explain over-parameterized language models, the model-agnostic analysis is investigated on recent deep neural methods. without modifying the black-box models, the researchers analyze the immediate feature of the neural layers, attention distribution, and so on @xcite @xcite . quantitative experiments and visual analysis is able to partially reveal the behaviors of the key components and the overall response of the methods to certain patterns @xcite . however, without any interpretable optimization of the model, the analysis is unable to provide enough detail for understanding @xcite , which indicates a completely faithful explanation from black-box components or deep neural methods is impossible. different from model-agnostic analysis, methods that integrate interpretable algorithms enable more comprehensive interpretability. two different types of structures are adopted in these approaches, including (1) a black-box backbone with an interpretable bypass for implicit informing and (2) a transparent backbone with an interpretable algorithm for direct interpreting @xcite . for implicitly informed methods, the introduced interpretable knowledge regulates the immediate feature or embedding @xcite . within the interpretable bypass, the performance of the backbone is maximally preserved, which is the main reason these structures are often opted for over-parameterized models @xcite . however, the integrated knowledge is unable to decisively change the structure of the backbones for a transparent decision process, which adversely affects the generalization ability of the approaches and limits the application of the methods to specific tasks. in contrast, approaches with interpretable backbones exhibit a more integrated relationship between the components, enabling better explanations than the above two types of approaches. the interpretable algorithms serve as word embedding, immediate feature, or the classifier to realize transparency decision process @xcite @xcite . but the performance of existing interpretable models remains incomparable to the most advanced language models. in this work, we address the aforementioned obstacles by developing a novel interpretable language modeling method by constructing a word-context-coupled space (w2cspace) aligned with statistical knowledge foot_0 , which enables (1) effective interpretation of bert representations @xcite by introducing the interpretable statistical logic, (2) reasonable context abstraction with the coupled word-level semantics, and (3) interpretable modeling for the given text with the context-relative distance. w2cspace serves as the key component in the backbone of our language modeling method, which realizes a decisive transparency increasing compared with the modelagnostic and implicitly informed methods. the structure of our method is illustrated in figure 1 . specifically, our main contributions can be summarized as follows: ## conclusion an interpretable language model is proposed by constructing a word-context-coupled space in this study. within w2cspace, (1) the uninterpretable neural representation in bert is regulated by interpretable associative knowledge relations, (2) an intermediate representation space with reasonable interpretable semantics is designed, (3) an interpretable semantic feature is introduced based on intermediate representation space for downstream classifiers. obviously, the above strategies bring a strong generalization ability for the interpretable pre-trained language modeling process. besides, for the potential risk preventing, the interpretable machine learning method is introduced for migrating the adverse affects from the black-box structure. moreover, in our method, the controllable decision process realize the regulation for the illegal language inputting by controlling the related context, and the strong cooperation between pretrained models and w2cspace can protect the parameter privacy from the data stealing. nevertheless, w2cspace is unable to directly handle high-level semantics, including sentences, paragraphs, and so on. even the word-level language models act as the mainstream methods in nlp, the above limitation should be further considered in the future. besides, restricted by our knowledge and efforts, the main experiments cannot cover all common tasks and all pre-trained models in nlp. relatively, the token-and sequence-level classifications have demonstrated attractive experimental performance on most nlp tasks. next, we also plan to extend w2cspace to more nlp tasks and find its more specific value.