Do politically charged terms with similar referents, like "undocumented immigrants" (UI) "illegal aliens" (IA) differ only in who uses them, or also in what they mean? We investigate usage patterns by projecting contextual embeddings into interpretable psycholinguistic feature space, and extracting narrative scenes with LLMs. We find that in partisan news, the term IA appears in contexts emphasizing causation and fear. UI appears in contexts emphasizing consequences experienced and shared humanity. Scene abstraction reveals parallel patterns: IA is embedded in narratives of criminality and threat, UI in narratives of vulnerability and governance. Beyond indexing speaker identity, these terms impart different construals on migrants: as agents of harm versus patients of circumstance. This dual-track methodology adds new tools to the growing body of computational approaches for understanding the conceptual framing of politically charged topics.

We study semantic construal in grammatical constructions using large language models. First, we project contextual word embeddings into three interpretable semantic spaces, each defined by a different set of psycholinguistic feature norms. We validate these interpretable spaces and then use them to automatically derive semantic characterizations of lexical items in two grammatical constructions: nouns in subject or object position within the same sentence, and the AANN construction (e.g., ‘a beautiful three days’). We show that a word in subject position is interpreted as more agentive than the very same word in object position, and that the nouns in the AANN construction are interpreted as more measurement-like than when in the canonical alternation. Our method can probe the distributional meaning of syntactic constructions at a templatic level, abstracted away from specific lexemes.

Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability. Here, we describe the approach of the team “longhorns” on Task 1 of the First Workshop on Dynamic Adversarial Data Collection (DADC), which asked teams to manually fool a model on an Extractive Question Answering task. Our team finished first (pending validation), with a model error rate of 62%. We advocate for a systematic, linguistically informed approach to formulating adversarial questions, and we describe the results of our pilot experiments, as well as our official submission.

This paper investigates contextual language models, which produce token representations, as a resource for lexical semantics at the word or type level. We construct multi-prototype word embeddings from bert-base-uncased (Devlin et al., 2018). These embeddings retain contextual knowledge that is critical for some type-level tasks, while being less cumbersome and less subject to outlier effects than exemplar models. Similarity and relatedness estimation, both type-level tasks, benefit from this contextual knowledge, indicating the context-sensitivity of these processes. BERT’s token level knowledge also allows the testing of a type-level hypothesis about lexical abstractness, demonstrating the relationship between token-level phenomena and type-level concreteness ratings. Our findings provide important insight into the interpretability of BERT: layer 7 approximates semantic similarity, while the final layer (11) approximates relatedness.