Author: Zen and the Art of Computer Programming
In the field of text generation, a variety of text generation models have been proposed, including RNN, GPT and other models. These models can achieve text generation tasks based on language models, but they are limited to single-modal text generation, that is, the input is only It can be a text sequence; and it cannot handle the fusion of text information of two or more modalities, so this paper will explore the method of cross-modal text reasoning and generation (CMT) based on deep learning.
Cross-modal text reasoning and generation, or MTL (Multi-Task Learning) model, refers to a model that predicts text data of different modalities at the same time, and uses this information for reasoning and generation. The MTL model can better capture the semantic relationship of different modalities, so as to better understand the meaning of the input text, so that the text generation model can generate novel and meaningful content with multi-modal characteristics.
Compared with the traditional unimodal text generation model, the MTL model has the following advantages:
- More comprehensive and adequate representation capabilities: Traditional unimodal text generation models can only process input information from one modality, so there are certain limitations in their generation quality. The MTL model can utilize the information of multiple modalities, so it has a more comprehensive and sufficient representation ability.
- Richer expression ability: Traditional unimodal text generation models can only generate texts that appear in a certain pattern, but cannot create new and unique expressions. The MTL model can create new and unique expressions, so the generated text is richer.
- Higher reasoning performance: Traditional unimodal text generation models can only perform information extraction and modeling on the input text, and then perform text generation, but lack a complete reasoning process. The MTL model can make full use of multi-modal semantic information to perform more accurate reasoning on the input text and generate higher-quality text output.
With the continuous emergence of multimodal text generation tasks, more and more researchers try to develop a text generation model with multimodal reasoning ability. However, the MTL model is still in the stage of theoretical exploration, and there is no fully mature model yet