Challenge: a key intent behind many emails is to get a reply from the recipient.
Approach: They propose to model the intents, expectations, and responsiveness in email exchanges by using a dataset containing 1800 emails annotated with nuanced types of intents and expectations.
Outcome: The proposed model is based on 1800 emails annotated with nuanced types of intents and expectations . it shows that social status, argumentation, and strength of social connection influence email response rates .

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Causal Effects of Linguistic Properties (2021.naacl-main)

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Challenge: Social scientists have long been interested in the causal effects of language, studying questions like: How should political candidates describe their personal history to appeal to voters?
Approach: They propose an algorithm for estimating causal effects of linguistic properties that leverages distant supervision and a pre-trained language model to adjust for the text.
Outcome: The proposed method outperforms other methods when estimating the effect of Amazon review sentiment on semi-simulated sales figures.
MailEx: Email Event and Argument Extraction (2023.emnlp-main)

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Challenge: Existing work on email event extraction only covers one specific aspect of email information and cannot connect with other relevant tasks.
Approach: They propose a new taxonomy for performing event extraction from conversational email threads.
Outcome: The proposed taxonomy covers 10 event types and 76 arguments in the email domain.
When does text prediction benefit from additional context? An exploration of contextual signals for chat and email messages (2021.naacl-industry)

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Challenge: Prior-message context provides the greatest lift in Teams (chat) scenario.
Approach: They compare prior-message context with email and chat messages from Microsoft Teams and Outlook.
Outcome: The proposed model outperforms existing models on two of the largest commercial communication platforms: Microsoft Teams and Outlook.
Crawling and Preprocessing Mailing Lists At Scale for Dialog Analysis (2020.acl-main)

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Challenge: a new neural segmentation model is used to segment 153 million emails . email is perhaps the most reliable and ubiquitous means of digital communication .
Approach: They present a new neural segmentation model that crawls 153 million emails . it achieves 96% accuracy on 15 classes of email segments .
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Causal Inference for Human-Language Model Collaboration (2024.naacl-long)

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Challenge: In this paper, we examine the collaborative dynamics between humans and language models where the interaction involves LMs proposing text segments and humans editing or responding to these segments.
Approach: They propose a causal estimand to estimate the incremental stylistic effect (ISE) of various interaction strategies in dynamic human-LM collaborations.
Outcome: The proposed estimand reduces confounding and significantly improves counterfactual estimation over a set of competitive baselines.
PROMINET: Prototype-based Multi-View Network for Interpretable Email Response Prediction (2023.emnlp-industry)

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Challenge: a new study examines email marketing performance by considering email content and metadata.
Approach: They propose a model that incorporates semantic and structural information from email data to generate latent exemplars for email response prediction.
Outcome: The proposed model outperforms baseline models on two real-world email datasets . it provides interpretability through prototypes at different granularity levels while maintaining comparable performance to non-interpretable models.
How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning (2023.emnlp-main)

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Challenge: Existing models excel at capturing semantic correlations within utterance embeddings but fail to determine specific causal relationships.
Approach: They propose to incorporate i.i.d. noise terms into conversation process to build a structural causal model . they propose to use unstructured conversation data to facilitate deep learning .
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EmailSum: Abstractive Email Thread Summarization (2021.acl-long)

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Challenge: Recent years have brought about interest in the task of summarizing conversation threads.
Approach: They develop an email thread summarization dataset that contains human-annotated short and long email threads over a wide variety of topics.
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Are Large Language Models (LLMs) Good Social Predictors? (2024.findings-emnlp)

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Challenge: Existing studies suggest that Large Language Models can generate human-like responses, but it is unclear how well they work and where the plausible predictions derive from.
Approach: They propose to use LLMs to generate human-like responses by mutability and accessibility of social inputs to perform a social prediction task.
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Bridging Internal Consistency and External Alignment: A Causal and Dynamic Interpretability Framework for LLM Generation (2026.acl-long)

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Challenge: Existing interpretability methods focus on internal and external aspects of the model . existing explanations often focus on surface correlations or static dependencies .
Approach: They propose a causal and dynamic interpretability framework for Large Language Models . they characterize backdoor-adjusted causal effects of generated prefix and prompt .
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