Health influencers play a growing role in shaping public beliefs 1 , yet their content is often conveyed through conversational narratives and rhetorical strategies rather than explicit factual claims.As a result, claimcentric verification methods struggle to capture the pragmatic meaning of influencer discourse.In this paper, we propose TAIGR (Takeaway Argumentation Inference with Grounded References), a structured framework designed to analyze influencer discourse, which operates in 3 stages: (1) identifying the core influencer recommendation-takeaway; (2) constructing an argumentation graph that captures influencer justification for the takeaway; (3) performing factor graph-based probabilistic inference to validate the takeaway.We evaluate TAIGR on a content validation task over influencer video transcripts on health, showing that accurate validation requires modeling the discourse's pragmatic and argumentative structure rather than treating transcripts as flat collections of claims.

Rajkumar Pujari, Dan Goldwasser. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Political discourse on social media often contains similar language with opposing intended meanings.For example, the phrase thoughts and prayers, is used to express sympathy for mass shooting victims, as well as satirically criticize the lack of legislative action on gun control.Understanding such discourse fully by reading only the text is difficult.However, knowledge of the social context information makes it easier.We characterize the social context required to fully understand such ambiguous discourse, by grounding the text in real-world entities, actions, and attitudes.We propose two datasets that require an understanding of social context and benchmark them using large pre-trained language models and several novel structured models.We show that structured models, explicitly modeling social context, outperform larger models on both tasks, but still lag significantly behind human performance.Finally, we perform an extensive analysis, to obtain further insights into the language understanding challenges posed by our social grounding tasks.

As large Pre-trained Language Models (PLMs) trained on large amounts of data in an unsupervised manner become more ubiquitous, identifying various types of bias in the text has come into sharp focus.Existing 'Stereotype Detection' datasets mainly adopt a diagnostic approach toward large PLMs.Blodgett et al. (2021a) show that there are significant reliability issues with the existing benchmark datasets.Annotating a reliable dataset requires a precise understanding of the subtle nuances of how stereotypes manifest in text.In this paper, we annotate a focused evaluation set for 'Stereotype Detection' that addresses those pitfalls by de-constructing various ways in which stereotypes manifest in text.Further, we present a multi-task model that leverages the abundance of data-rich neighboring tasks such as hate speech detection, offensive language detection, misogyny detection, etc., to improve the empirical performance on 'Stereotype Detection'.We then propose a reinforcement-learning agent that guides the multi-task learning model by learning to identify the training examples from the neighboring tasks that help the target task the most.We show that the proposed models achieve significant empirical gains over existing baselines on all the tasks.

Politicians often have underlying agendas when reacting to events.Arguments in contexts of various events reflect a fairly consistent set of agendas for a given entity.In spite of recent advances in Pretrained Language Models, those text representations are not designed to capture such nuanced patterns.In this paper, we propose a Compositional Reader model consisting of encoder and composer modules, that captures and leverages such information to generate more effective representations for entities, issues, and events.These representations are contextualized by tweets, press releases, issues, news articles, and participating entities.Our model processes several documents at once and generates composed representations for multiple entities over several issues or events.Via qualitative and quantitative empirical analysis, we show that these representations are meaningful and effective.

In this paper, we propose a hybrid technique for semantic question matching. It uses a proposed two-layered taxonomy for English questions by augmenting state-of-the-art deep learning models with question classes obtained from a deep learning based question classifier. Experiments performed on three open-domain datasets demonstrate the effectiveness of our proposed approach. We achieve state-of-the-art results on partial ordering question ranking (POQR) benchmark dataset. Our empirical analysis shows that coupling standard distributional features (provided by the question encoder) with knowledge from taxonomy is more effective than either deep learning or taxonomy-based knowledge alone.