Papers with linguistic alignment
Alignment, Acceptance, and Rejection of Group Identities in Online Political Discourse (N18-4)
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| Challenge: | linguistic alignment is a robust and robust form of communication accommodation, and has been detected in a variety of linguistic interactions, ranging from speed dates to the Supreme Court. |
| Approach: | They propose a model to examine alignment in Twitter conversations across antagonistic groups. |
| Outcome: | The proposed model adapts the WHAM alignment model to examine alignment in Twitter conversations across antagonistic groups. |
Not that much power: Linguistic alignment is influenced more by low-level linguistic features rather than social power (P18-1)
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| Challenge: | linguistic alignment between interlocutors of higher power is attributed to their relative social power, but studies on low-level linguistic features do not account for these factors. |
| Approach: | They characterize the effect of power on alignment with logistic regression models in two datasets and find it vanishes after controlling for low-level features such as utterance length. |
| Outcome: | The proposed model shows that the effect vanishes or is reversed after controlling for low-level features such as utterance length. |
What Makes a Good Counselor? Learning to Distinguish between High-quality and Low-quality Counseling Conversations (P19-1)
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| Challenge: | Qualitative counseling relies on active collaboration between clients and counselors . |
| Approach: | They propose to use linguistic features to capture differences between high- and low-quality counseling conversations to build automatic classifiers that can predict counseling quality with accuracies of up to 88%. |
| Outcome: | The proposed model can predict counseling quality with accuracies of up to 88%. |
Calibrating Beyond English: Language Diversity for Better Quantized Multilingual LLMs (2026.eacl-long)
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| Challenge: | Existing quantization methods typically use small, English-only calibration sets . however, their impact on multilingual models remains underexplored . |
| Approach: | They evaluate eight calibration settings across two quantizers on data from 10 different languages. |
| Outcome: | The results show that tailoring calibration sets to the evaluation language yields the largest improvements for individual languages, underscoring the importance of linguistic alignment. |
Detecting Bot-Generated Text by Characterizing Linguistic Accommodation in Human-Bot Interactions (2021.findings-acl)
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| Challenge: | Language generation models' democratization makes it easier to generate human-like text at-scale for nefarious activities, from spreading misinformation to targeting specific groups with hate speech. |
| Approach: | They propose to use linguistic alignment to detect bot-generated text rather than using it directly. |
| Outcome: | The proposed methods are more robust across datasets and models if they use information about how people respond to it rather than using the bot's text directly. |
Linguistic Alignment Predicts Learning in Small Group Tutoring Sessions (2025.findings-emnlp)
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| Challenge: | Cognitive science offers rich theories of learning and communication, yet these are often difficult to operationalize at scale. |
| Approach: | They investigate linguistic alignment in a longitudinal dataset of real-world tutoring interactions and associated student test scores. |
| Outcome: | The proposed method can be applied to real-world tutoring interactions and student test scores. |
Balanced Multi-Factor In-Context Learning for Multilingual Large Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches address key factors that influence multilingual ICL, but they do not integrate them into the model. |
| Approach: | They propose a method that quantifies and optimally balances three factors for improved example selection. |
| Outcome: | Experiments on mCSQA and TYDI show that the proposed method outperforms existing methods. |
ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering (2026.acl-long)
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| Challenge: | Existing serialization methods fail to capture explicit hierarchies and lack schema flexibility . Existing tree-based approaches suffer from limited semantic adaptability . |
| Approach: | They propose a method that leverages the global semantic awareness of LLMs to reconstruct tables into Logical Semantic Trees. |
| Outcome: | The proposed method achieves state-of-the-art (SOTA) performance on complex table benchmarks. |