Engineering and Information Technology Collected Works - Research Publications
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ItemProbing Power by Prompting: Harnessing Pre-trained Language Models for Power Connotation Framing(Association for Computational Linguistics, 2023)When describing actions, subtle changes in word choice can evoke very different associations with the involved entities. For instance, a company ‘employing workers’ evokes a more positive connotation than the one ‘exploiting’ them. This concept is called connotation. This paper investigates whether pre-trained language models (PLMs) encode such subtle connotative information about power differentials between involved entities. We design a probing framework for power connotation, building on (CITATION)’s operationalization of connotation frames. We show that zero-shot prompting of PLMs leads to above chance prediction of power connotation, however fine-tuning PLMs using our framework drastically improves their accuracy. Using our fine-tuned models, we present a case study of power dynamics in US news reporting on immigration, showing the potential of our framework as a tool for understanding subtle bias in the media.
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ItemIncorporating Syntax and Semantics in Coreference Resolution with Heterogeneous Graph Attention Network(Association for Computational Linguistics, 2021-01-01)
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ItemExploiting Worker Correlation for Label Aggregation in Crowdsourcing(International Machine Learning Society, 2019)Crowdsourcing has emerged as a core component of data science pipelines. From collected noisy worker labels, aggregation models that incorporate worker reliability parameters aim to infer a latent true annotation. In this paper, we argue that existing crowdsourcing approaches do not sufficiently model worker correlations observed in practical settings; we propose in response an enhanced Bayesian classifier combination (EBCC) model, with inference based on a mean-field variational approach. An introduced mixture of intra-class reliabilities---connected to tensor decomposition and item clustering---induces inter-worker correlation. EBCC does not suffer the limitations of existing correlation models: intractable marginalisation of missing labels and poor scaling to large worker cohorts. Extensive empirical comparison on 17 real-world datasets sees EBCC achieving the highest mean accuracy across 10 benchmark crowdsourcing methods.