S ente C on: Leveraging Lexicons to Learn Human-Interpretable Language Representations (2023)

Abstract: Although deep language representations have become the dominant form of language featurization in recent years, in many settings it is important to understand a model’s decision-making process. This necessitates not only an interpretable model but also interpretable features. In particular, language must be featurized in a way that is interpretable while still characterizing the original text well. We present SenteCon, a method for introducing human interpretability in deep language representations. Given a passage of text, SenteCon encodes the text as a layer of interpretable categories in which each dimension corresponds to the relevance of a specific category. Our empirical evaluations indicate that encoding language with SenteCon provides high-level interpretability at little to no cost to predictive performance on downstream tasks. Moreover, we find that SenteCon outperforms existing interpretable language representations with respect to both its downstream performance and its agreement with human characterizations of the text.

## introduction deep language representations have become the dominant form of language featurization in recent years. these black-box representations perform excellently on a diverse array of tasks and are widely used in state-of-the-art machine learning pipelines. in many settings, however, it is important to understand a model's decision-making process, which necessitates not only an interpretable model but also interpretable features. to be useful, language must be featurized in a way that is interpretable while still characterizing the original text well. the fields of affective computing, computational social science, and computational psychology often use models to elucidate the relationships between patterns of language use and specific outcomes @xcite . moreover, interpretability is necessary to enforce desirable criteria like fairness @xcite , robustness @xcite , and causality @xcite . despite advances in deep language representations, they are not considered human-interpretable due to their high dimensionality and the fact that their dimensions do not correspond to humanunderstandable concepts. instead, researchers in need of interpretable language representations often turn to lexicons @xcite @xcite , which map words to meaningful categories or concepts. while useful in their simplicity, lexicons capture much less information about the text than do deep language representations. most notably, because they parse text on the level of individual words, lexicons are unable to represent how those words are used within the broader context of the text, which can lead to misrepresentation of the text's meaning or intent. consequently, lexicon-based language representations may not necessarily correspond well with how a human, who is able to comprehend the entire passage context, would perceive the text; and they may not perform well when used in downstream tasks. with an eye toward addressing these concerns, we present sentecon, foot_0 a method for introducing human interpretability in deep language representations. given a sentence, foot_1 sentecon encodes the text as a layer of interpretable categories in which each dimension corresponds to the relevance of a specific category (figure 1 ). the output of sentecon can itself therefore be viewed as an interpretable language representation. as language use can vary across text domains, we also present an extension, sentecon+, that can adapt to specific domains via a reference corpus, a collection of unlabeled text passages from a target domain. we evaluate sentecon and sentecon+ (jointly denoted hereafter as sentecon(+)) with respect to both human interpretability and empirical performance. we first conduct an extensive human study that measures how well sentecon(+) characterizes text compared to traditional lexicons. we complement this study with experiments using sentecon(+) interpretable representations in downstream tasks, which allow us to compare its performance with that of existing interpretable and non-interpretable language representations. finally, we analyze sentecon(+) representations to determine whether they indeed are influenced by sentence context in a meaningful way. ## sentecon(+) sentecon(+) draws upon the notion of the lexicon; however, rather than mapping words to categories, sentecon(+) maps the categories to the deep embeddings of sentences that contain those words. this, in effect, automatically generates dictionaries of sentence embeddings. to encode the categories of a new sentence, sentecon(+) uses the similarity between the embedding of the new sentence and the embeddings of the sentences associated with each category. generally, sentecon(+) can be thought of as two parts: (1) building a sentence embedding dictionary and (2) using that dictionary to generate an interpretable representation for a new sentence. we describe the details of the procedure in sections 3.1 and 3.2. the full sentecon(+) method is formally outlined in algorithm 1. ## experimental setup to assess the utility of sentecon and sente-con+, we evaluate both methods to determine how well they characterize text in comparison to both existing lexicon-based methods and deep language models. when computing sentecon(+) representations, we use mpnet @xcite as our deep language model m θ and cosine similarity as our similarity metric g. our experiments consist of both human evaluations of sentecon(+) language representations and tests of performance when using them in downstream predictive tasks. ## conclusion in this paper we introduced sentecon, a humaninterpretable language representation that captures sentence context while retaining the benefits of interpretable lexicons. we conducted human evaluations to determine the agreement between sente-con representations and the actual content of the text, and we ran a series of experiments using sen-tecon in downstream predictive tasks. in doing so, we demonstrated that sentecon and its extension, sentecon+, better represent the character and content of the text than traditional lexicons do. furthermore, we showed that when used in conjunction with language models fine-tuned on the downstream task, sentecon and sentecon+ provide interpretability to deep language models without any loss of performance. these findings render sentecon and sentecon+ compelling candidates for problems in fields like medicine, social science, and psychology, where understanding language use is an important part of the scientific process and where insight into a model's decisionmaking process can be paramount.