Understanding confounding effects in linguistic coordination: an information-theoretic approach
We suggest an information-theoretic approach for measuring stylistic coordination in dialogues. The proposed measure has a simple predictive interpretation and can account for various confounding factors through proper conditioning. We revisit some of the previous studies that reported strong signatures of stylistic accommodation, and find that a significant part of the observed coordination can be attributed to a simple confounding effect - length coordination. Specifically, longer utterances tend to be followed by longer responses, which gives rise to spurious correlations in the other stylistic features. We propose a test to distinguish correlations in length due to contextual factors (topic of conversation, user verbosity, etc.) and turn-by-turn coordination. We also suggest a test to identify whether stylistic coordination persists even after accounting for length coordination and contextual factors.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Deconfounding age effects with fair representation learning when assessing dementia
One of the most prevalent symptoms among the elderly population, dementia, can be detected by classifiers trained on linguistic features extracted from narrative transcripts. However, these linguistic features are impact…
Representation LearningEmbodiment-Induced Coordination Regimes in Tabular Multi-Agent Q-Learning
Centralized value learning is often assumed to improve coordination and stability in multi-agent reinforcement learning, yet this assumption is rarely tested under controlled conditions. We directly evaluate it in a full…
Multi-agent Reinforcement LearningRepresentation LearningDetecting and Measuring Confounding Using Causal Mechanism Shifts
Detecting and measuring confounding effects from data is a key challenge in causal inference. Existing methods frequently assume causal sufficiency, disregarding the presence of unobserved confounding variables. Causal s…
Causal DiscoveryCausal InferenceCausal Discovery and Inference towards Urban Elements and Associated Factors
To uncover the city's fundamental functioning mechanisms, it is important to acquire a deep understanding of complicated relationships among citizens, location, and mobility behaviors. Previous research studies have appl…
Causal DiscoveryMitigating Confounding in Speech-Based Dementia Detection through Weight Masking
Deep transformer models have been used to detect linguistic anomalies in patient transcripts for early Alzheimer's disease (AD) screening. While pre-trained neural language models (LMs) fine-tuned on AD transcripts perfo…