skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Facilitating the Communication of Politeness through Fine-Grained Paraphrasing
Aided by technology, people are increasingly able to communicate across geographical, cultural, and language barriers. This ability also results in new challenges, as interlocutors need to adapt their communication approaches to increasingly diverse circumstances. In this work, we take the first steps towards automatically assisting people in adjusting their language to a specific communication circumstance. As a case study, we focus on facilitating the accurate transmission of pragmatic intentions and introduce a methodology for suggesting paraphrases that achieve the intended level of politeness under a given communication circumstance. We demonstrate the feasibility of this approach by evaluating our method in two realistic communication scenarios and show that it can reduce the potential for misalignment between the speaker’s intentions and the listener’s perceptions in both cases.  more » « less
Award ID(s):
1750615 1910147
PAR ID:
10216850
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Page Range / eLocation ID:
5127 to 5140
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, CHI EA '23: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems)
    AI language technologies increasingly assist and expand human communication. While AI-mediated communication reduces human effort, its societal consequences are poorly understood. In this study, we investigate whether using an AI writing assistant in personal self-presentation changes how people talk about themselves. In an online experiment, we asked participants (N=200) to introduce themselves to others. An AI language assistant supported their writing by suggesting sentence completions. The language model generating suggestions was fine-tuned to preferably suggest either interest, work, or hospitality topics. We evaluate how the topic preference of a language model affected users’ topic choice by analyzing the topics participants discussed in their self-presentations. Our results suggest that AI language technologies may change the topics their users talk about. We discuss the need for a careful debate and evaluation of the topic priors built into AI language technologies. 
    more » « less
  2. ; ; (, Proceedings of the ACM on humancomputer interaction)
    Communication tools such as email facilitate communication and collaboration between speakers of different languages, who use two primary strategies—English as a common language and machine translation (MT) tools—to help them overcome language barriers. However, each of these communication strategies creates its own challenges for cross-lingual communication. In this paper, we compare how people’s interpretations of an email sender’s social intention, and their evaluation of the email and the senders, differ when using a common language versus MT in email communication. We conducted an online experiment in which monolingual native English speakers read and rated request emails written by native English speakers, emails written by bilingual Chinese speakers in English, and emails written in Chinese then machine-translated into English. We found that participants interpreted the social intentions of the email sender less accurately for machine-translated emails than for emails written by non-native speakers in English. Participants also rated the senders and emails less positively overall for machine-translated emails compared to emails written by non-native speakers in English. Based on these findings, we suggest design possibilities that could better aid multilingual communication. 
    more » « less
  3. ; ; ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication. Speaker intentions often vary dynamically depending on different nonverbal contexts, such as vocal patterns and facial expressions. As a result, when modeling human language, it is essential to not only consider the literal meaning of the words but also the nonverbal contexts in which these words appear. To better model human language, we first model expressive nonverbal representations by analyzing the fine-grained visual and acoustic patterns that occur during word segments. In addition, we seek to capture the dynamic nature of nonverbal intents by shifting word representations based on the accompanying nonverbal behaviors. To this end, we propose the Recurrent Attended Variation Embedding Network (RAVEN) that models the fine-grained structure of nonverbal subword sequences and dynamically shifts word representations based on nonverbal cues. Our proposed model achieves competitive performance on two publicly available datasets for multimodal sentiment analysis and emotion recognition. We also visualize the shifted word representations in different nonverbal contexts and summarize common patterns regarding multimodal variations of word representations. 
    more » « less
  4. ; ; ; ; (, HRI '21: Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction)
    null (Ed.)
    Augmentative and alternative communication (AAC) devices enable speech-based communication. However, AAC devices do not support nonverbal communication, which allows people to take turns, regulate conversation dynamics, and express intentions. Nonverbal communication requires motion, which is often challenging for AAC users to produce due to motor constraints. In this work, we explore how socially assistive robots, framed as ''sidekicks,'' might provide augmented communicators (ACs) with a nonverbal channel of communication to support their conversational goals. We developed and conducted an accessible co-design workshop that involved two ACs, their caregivers, and three motion experts. We identified goals for conversational support, co-designed prototypes depicting possible sidekick forms, and enacted different sidekick motions and behaviors to achieve speakers' goals. We contribute guidelines for designing sidekicks that support ACs according to three key parameters: attention, precision, and timing. We show how these parameters manifest in appearance and behavior and how they can guide future designs for augmented nonverbal communication. 
    more » « less
  5. ; ; ; ; (, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)
    Connected and automated vehicles (CAVs) offer many potential advantages, including improved traffic flow, reduction of traffic accidents, and increased freedom for adolescents and adults with restricted mobility. However, successful implementation of CAVs depends on several factors, especially acceptance and preferences by people. Specifically, during the earlier stage of deployment, CAVs will have to share the roads with human-driven vehicles (HDVs), which requires communication between CAVs and HDVs regarding their intentions and future actions. Therefore, as a first step in our research program, we conducted a survey of 182 U.S. drivers to assess their knowledge of CAVs and their thoughts about implementation. We report the survey results, accompanied by our interpretations. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email