Understanding the implicit values and beliefs of diverse groups and cultures using qualitative texts – such as long-form narratives – and domain-expert interviews is a fundamental goal of social anthropology. This paper builds upon a 2022 study that introduced the NLP task of Recognizing Value Resonance (RVR) for gauging perspective – positive, negative, or neutral – on implicit values and beliefs in textual pairs. This study included a novel hand-annotated dataset, the World Values Corpus (WVC), designed to simulate the task of RVR, and a transformer-based model, Resonance-Tuned RoBERTa, designed to model the task. We extend existing work by refining the task definition and releasing the World Values Corpus (WVC) dataset. We further conduct several validation experiments designed to robustly evaluate the need for task specific modeling, even in the world of LLMs. Finally, we present two additional Resonance-Tuned models trained over extended RVR datasets, designed to improve RVR model versatility and robustness. Our results demonstrate that the Resonance-Tuned models outperform top-performing Recognizing Textual Entailment (RTE) models in recognizing value resonance as well as zero-shot GPT-3.5 under several different prompt structures, emphasizing its practical applicability. Our findings highlight the potential of RVR in capturing cultural values within texts and the importance of task-specific modeling.

Resolving Indirect Speech Acts (ISAs), in which the intended meaning of an utterance is not identical to its literal meaning, is essential to enabling the participation of intelligent systems in peoples’ everyday lives. Especially challenging are those cases in which the interpretation of such ISAs depends on context. To test a system’s ability to perform ISA resolution we need a corpus, but developing such a corpus is difficult, especialy given the contex-dependent requirement. This paper addresses the difficult problems of constructing a corpus of ISAs, taking inspiration from relevant work in using corpora for reasoning tasks. We present a formal representation of ISA Schemas required for such testing, including a measure of the difficulty of a particular schema. We develop an approach to authoring these schemas using corpus analysis and crowdsourcing, to maximize realism and minimize the amount of expert authoring needed. Finally, we describe several characteristics of collected data, and potential future work.

We perform a corpus analysis to develop a representation of the knowledge and reasoning used to interpret indirect speech acts. An indirect speech act (ISA) is an utterance whose intended meaning is different from its literal meaning. We focus on those speech acts in which slight changes in situational or contextual information can switch the dominant intended meaning of an utterance from direct to indirect or vice-versa. We computationalize how various contextual features can influence a speaker’s beliefs, and how these beliefs can influence the intended meaning and choice of the surface form of an utterance. We axiomatize the domain-general patterns of reasoning involved, and implement a proof-of-concept architecture using Answer Set Programming. Our model is presented as a contribution to cognitive science and psycholinguistics, so representational decisions are justified by existing theoretical work.