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Justices on the United States Supreme Court use rhetorical strategies to maintain institutional legitimacy. In the court opinion, a strategy called the monologic voice presents a flattering depiction of the Court. The monologic voice occurs through two tones, the individualistic and collective, which respectively maintain the Justices’ legitimacy through critique and the Court’s legitimacy through unification. We train large language models to identify these rhetorical features in 15,291 modern Supreme Court opinions, issued between 1946 and 2022. While the fraction of collective and individualistic tones has been relatively consistent between 1946 and 2022, the Rehnquist Court used the collective tone at a higher rate than any other Court. In recent terms, 2021 and 2022, we find suggestions of another rhetorical shift, as all Associate Justices of the Roberts Court, excluding Chief Justice Roberts, used the individualistic tone at a historically high rate. 

The language used by US courtroom actors in criminal trials has long been studied for biases. However, systematic studies for bias in high-stakes court trials have been difficult, due to the nuanced nature of bias and the legal expertise required. Large language models offer the possibility to automate annotation. But validating the computational approach requires both an understanding of how automated methods fit in existing annotation workflows and what they really offer. We present a case study of adding a computational model to a complex and high-stakes problem: identifying gender-biased language in US capital trials for women defendants.Our team of experienced death-penalty lawyers and NLP technologists pursue a three-phase study: first annotating manually, then training and evaluating computational models, and finally comparing expert annotations to model predictions. Unlike many typical NLP tasks, annotating for gender bias in months-long capital trials is complicated, with many individual judgment calls. Contrary to standard arguments for automation that are based on efficiency and scalability, legal experts find the computational models most useful in providing opportunities to reflect on their own bias in annotation and to build consensus on annotation rules. This experience suggests that seeking to replace experts with computational models for complex annotation is both unrealistic and undesirable. Rather, computational models offer valuable opportunities to assist the legal experts in annotation-based studies. 

Through a computational reading of the online book reviewing community LibraryThing, we examine the dynamics of a collaborative tagging system and learn how its users refine and redefine literary genres. LibraryThing tags are overlapping and multi-dimensional, created in a shared space by thousands of users, including readers, bookstore owners, and librarians. A common understanding of genre is that it relates to the content of books, but this resource allows us to view genre as an intersection of user communities and reader values and interests. We explore different methods of computational genre measurement within the open space of user-created tags. We measure overlap between books, tags, and users, and we also measure the homogeneity of communities associated with genre tags and correlate this homogeneity with reviewing behavior.Finally, by analyzing the text of reviews, we identify the thematic signatures of genres on LibraryThing, revealing similarities and differences between them. These measurements are intended to elucidate the genre conceptions of the users, not, as in prior work, to normalize the tags or enforce a hierarchy. We find that LibraryThing users make sense of genre through a variety of values and expectations, many of which fall outside common definitions and understandings of genre. 

Much of the progress in contemporary NLP has come from learning representations, such as masked language model (MLM) contextual embeddings, that turn challenging problems into simple classification tasks. But how do we quantify and explain this effect? We adapt general tools from computational learning theory to fit the specific characteristics of text datasets and present a method to evaluate the compatibility between representations and tasks. Even though many tasks can be easily solved with simple bag-of-words (BOW) representations, BOW does poorly on hard natural language inference tasks. For one such task we find that BOW cannot distinguish between real and randomized labelings, while pre-trained MLM representations show 72x greater distinction between real and random labelings than BOW. This method provides a calibrated, quantitative measure of the difficulty of a classification-based NLP task, enabling comparisons between representations without requiring empirical evaluations that may be sensitive to initializations and hyperparameters. The method provides a fresh perspective on the patterns in a dataset and the alignment of those patterns with specific labels. 

A common factor in bias measurement methods is the use of hand-curated seed lexicons, but there remains little guidance for their selection. We gather seeds used in prior work, documenting their common sources and rationales, and in case studies of three English-language corpora, we enumerate the different types of social biases and linguistic features that, once encoded in the seeds, can affect subsequent bias measurements. Seeds developed in one context are often re-used in other contexts, but documentation and evaluation remain necessary precursors to relying on seeds for sensitive measurements. 

One commonly recognized feature of the Ancient Greek corpus is that later texts frequently imitate and allude to model texts from earlier time periods, but analysis of this phenomenon is mostly done for specific author pairs based on close reading and highly visible instances of imitation. In this work, we use computational techniques to examine the similarity of a wide range of Ancient Greek authors, with a focus on similarity between authors writing many centuries apart. We represent texts and authors based on their usage of high-frequency words to capture author signatures rather than document topics and measure similarity using Jensen- Shannon Divergence. We then analyze author similarity across centuries, finding high similarity between specific authors and across the corpus that is not common to all languages. 

Images can give us insights into the contextual meanings of words, but current image-text grounding approaches require detailed annotations. Such granular annotation is rare, expensive, and unavailable in most domain-specific contexts. In contrast, unlabeled multi-image, multi-sentence documents are abundant. Can lexical grounding be learned from such documents, even though they have significant lexical and visual overlap? Working with a case study dataset of real estate listings, we demonstrate the challenge of distinguishing highly correlated grounded terms, such as “kitchen” and “bedroom”, and introduce metrics to assess this document similarity. We present a simple unsupervised clustering-based method that increases precision and recall beyond object detection and image tagging baselines when evaluated on labeled subsets of the dataset. The proposed method is particularly effective for local contextual meanings of a word, for example associating “granite” with countertops in the real estate dataset and with rocky landscapes in a Wikipedia dataset.