Text embeddings are a fundamental component in many NLP tasks, including classification, regression, clustering, and semantic search.However, despite their ubiquitous application, challenges persist in interpreting embeddings and explaining similarities between them.In this work, we provide a structured overview of methods specializing in inherently interpretable text embeddings and text similarity explanation, an underexplored research area.We characterize the main ideas, approaches, and tradeoffs.We compare means of evaluation, discuss overarching lessons learned and finally identify opportunities and open challenges for future research.

An important aspect of responsible recommendation systems is the transparency of the prediction mechanisms. This is a general challenge for deep-learning-based systems such as the currently predominant neural news recommender architectures, which are optimized to predict clicks by matching candidate news items against users’ reading histories. Such systems achieve state-of-the-art click-prediction performance, but the rationale for their decisions is difficult to assess. At the same time, the economic and societal impact of these systems makes such insights very much desirable. In this article, we ask the question to what extent the recommendations of current news recommender systems are actually based on content-related evidence from reading histories. We approach this question from an explainability perspective. Building on the concept of integrated gradients, we present a neural news recommender that can accurately attribute individual recommendations to news items and words in input reading histories while maintaining a top scoring click-prediction performance. Using our method as a diagnostic tool, we find that: (a), a substantial number of users’ clicks on news are not explainable from reading histories, and many history-explainable items are actually skipped; (b), while many recommendations are based on content-related evidence in histories, for others the model does not attend to reasonable evidence, and recommendations stem from a spurious bias in user representations. Our code is publicly available at https://github.com/lucasmllr/xnrs .

Siamese encoders such as sentence transformers are among the least understood deep models.Established attribution methods cannot tackle this model class since it compares two inputs rather than processing a single one.To address this gap, we have recently proposed an attribution method specifically for Siamese encoders (Möller et al., 2023).However, it requires models to be adjusted and fine-tuned and therefore cannot be directly applied to off-the-shelf models.In this work, we reassess these restrictions and propose (i) a model with exact attribution ability that retains the original model's predictive performance and (ii) a way to compute approximate attributions for off-the-shelf models.We extensively compare approximate and exact attributions and use them to analyze the models' attendance to different linguistic aspects.We gain insights into which syntactic roles Siamese transformers attend to, confirm that they mostly ignore negation, explore how they judge semantically opposite adjectives, and find that they exhibit lexical bias.

Despite the success of Siamese encoder models such as sentence transformers (ST), little is known about the aspects of inputs they pay attention to.A barrier is that their predictions cannot be attributed to individual features, as they compare two inputs rather than processing a single one.This paper derives a local attribution method for Siamese encoders by generalizing the principle of integrated gradients to models with multiple inputs.The output takes the form of feature-pair attributions and in case of STs it can be reduced to a token-token matrix.Our method involves the introduction of integrated Jacobians and inherits the advantageous formal properties of integrated gradients: it accounts for the model's full computation graph and is guaranteed to converge to the actual prediction.A pilot study shows that in case of STs few token pairs can dominate predictions and that STs preferentially focus on nouns and verbs.For accurate predictions, however, they need to attend to the majority of tokens and parts of speech.