Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
41 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation (2105.14445v1)

Published 30 May 2021 in cs.CL

Abstract: Multi-modal dialog modeling is of growing interest. In this work, we propose frameworks to resolve a specific case of multi-modal dialog generation that better mimics multi-modal dialog generation in the real world, where each dialog turn is associated with the visual context in which it takes place. Specifically, we propose to model the mutual dependency between text-visual features, where the model not only needs to learn the probability of generating the next dialog utterance given preceding dialog utterances and visual contexts, but also the probability of predicting the visual features in which a dialog utterance takes place, leading the generated dialog utterance specific to the visual context. We observe significant performance boosts over vanilla models when the mutual dependency between text and visual features is modeled. Code is available at https://github.com/ShannonAI/OpenViDial.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (8)
  1. Shuhe Wang (18 papers)
  2. Yuxian Meng (37 papers)
  3. Xiaofei Sun (36 papers)
  4. Fei Wu (317 papers)
  5. Rongbin Ouyang (6 papers)
  6. Rui Yan (250 papers)
  7. Tianwei Zhang (199 papers)
  8. Jiwei Li (137 papers)
Citations (15)