mlsanityData to 7 Oct 2026

FaceChat: An Emotion-Aware Face-to-face Dialogue Framework

arXiv 2303.07316 ↗First posted 8 Mar 2023cs.CL cs.AIcited by 3

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Abstract (arXiv)

While current dialogue systems like ChatGPT have made significant advancements in text-based interactions, they often overlook the potential of other modalities in enhancing the overall user experience. We present FaceChat, a web-based dialogue framework that enables emotionally-sensitive and face-to-face conversations. By seamlessly integrating cutting-edge technologies in natural language processing, computer vision, and speech processing, FaceChat delivers a highly immersive and engaging user experience. FaceChat framework has a wide range of potential applications, including counseling, emotional support, and personalized customer service. The system is designed to be simple and flexible as a platform for future researchers to advance the field of multimodal dialogue systems. The code is publicly available at https://github.com/qywu/FaceChat.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 12 of its 33 references that are in the map. First posted on arXiv 8 Mar 2023.

Builds on

12 of 33 references.

References · 12

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  3. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
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  12. 2022Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue Systemcited by 16

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Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2023Fairness of ChatGPT and the Role Of Explainable-Guided Promptscited by 13
  2. 2024AV-EmoDialog: Chat with Audio-Visual Users Leveraging Emotional Cuescited by 2
  3. 2023Dialogue Systems Can Generate Appropriate Responses without the Use of Question Marks? -- Investigation of the Effects of Question Marks on Dialogue Systemscited by 0