arXiv:2402.07640v2 Announce Type: replace Abstract: The Controllable Multimodal Feedback Synthesis (CMFeed) dataset enables the generation of sentiment-controlled feedback from multimodal inputs. It contains images, text, human comments, comments’ metadata and sentiment labels. Existing datasets for related tasks such as multimodal summarization, visual question answering, visual dialogue, and sentiment-aware text generation do not incorporate training models using human-generated outputs and their metadata, a gap that CMFeed addresses. This capability is critical for developing feedback systems that understand and replicate human-like spontaneous responses. Based on the CMFeed dataset, we define a novel task of controllable feedback synthesis to generate context-aware feedback aligned with the desired sentiment. We propose a benchmark feedback synthesis system comprising encoder, decoder, and controllability modules. It employs transformer and Faster R-CNN networks to extract features and generate sentiment-specific feedback, achieving a sentiment classification accuracy of 77.23%, which is 18.82% higher than models not leveraging the dataset’s unique controllability features. Additionally, we incorporate a similarity module for relevance assessment through rank-based metrics.
The article titled “Controllable Multimodal Feedback Synthesis: A Dataset and Benchmark System” introduces the Controllable Multimodal Feedback Synthesis (CMFeed) dataset, which allows for the generation of sentiment-controlled feedback from multimodal inputs. Unlike existing datasets for related tasks, CMFeed incorporates human-generated outputs and their metadata, filling a crucial gap in training models for feedback systems that aim to replicate human-like spontaneous responses. The article defines a novel task of controllable feedback synthesis using the CMFeed dataset and proposes a benchmark feedback synthesis system comprising encoder, decoder, and controllability modules. The system utilizes transformer and Faster R-CNN networks to extract features and generate sentiment-specific feedback, achieving a sentiment classification accuracy of 77.23%. The article also incorporates a similarity module for relevance assessment through rank-based metrics. Overall, this article presents a comprehensive approach to developing feedback systems that can generate context-aware feedback aligned with desired sentiments.
Exploring Controllable Multimodal Feedback Synthesis with CMFeed Dataset
In the era of advanced natural language processing and computer vision, the ability to develop systems that understand and replicate human-like spontaneous responses is crucial. The Controllable Multimodal Feedback Synthesis (CMFeed) dataset brings us one step closer to achieving this goal. By incorporating training models with human-generated outputs and their metadata, CMFeed fills a significant gap in existing datasets for tasks like multimodal summarization, visual question answering, visual dialogue, and sentiment-aware text generation.
The CMFeed dataset is a rich collection of images, text, human comments, comments’ metadata, and sentiment labels. This diverse range of data allows us to generate sentiment-controlled feedback from multimodal inputs, enabling the synthesis of context-aware feedback aligned with desired sentiments. We define this as the novel task of controllable feedback synthesis.
To address this new task, we propose a benchmark feedback synthesis system that comprises three main modules: an encoder, a decoder, and a controllability module. The encoder leverages transformer and Faster R-CNN networks to extract relevant features from both textual and visual inputs. These features are then passed through the decoder, which generates sentiment-specific feedback.
One of the key advantages of our benchmark system is the integration of the controllability module. This module allows us to control the sentiment of the generated feedback, aligning it with the desired sentiment. Through extensive evaluation, our system achieves a sentiment classification accuracy of 77.23%, a remarkable 18.82% improvement compared to models that do not leverage the unique controllability features of the CMFeed dataset.
Furthermore, we also incorporate a similarity module into our system to assess the relevance of the generated feedback. This module utilizes rank-based metrics to measure the similarity between the generated feedback and the desired target feedback. By considering relevance, we can enhance the quality and effectiveness of the feedback synthesis process.
The CMFeed dataset and our benchmark feedback synthesis system open up exciting opportunities for various applications. By leveraging the dataset’s controllability features, we can create systems that generate context-aware feedback aligned with user-defined sentiments. This has implications for enhancing sentiment analysis, personalized recommendation systems, and even social media content generation.
In conclusion, the CMFeed dataset bridges a crucial gap in existing multimodal datasets by incorporating human-generated outputs and their metadata. By introducing the task of controllable feedback synthesis, we propose a benchmark feedback synthesis system that achieves impressive sentiment classification accuracy through the use of the CMFeed dataset’s unique controllability features. This innovative approach paves the way for further advancements and opportunities in the field of multimodal feedback synthesis.
The arXiv paper, titled “Controllable Multimodal Feedback Synthesis (CMFeed) Dataset and Benchmark System,” introduces a new dataset and benchmark system for generating sentiment-controlled feedback from multimodal inputs. This paper addresses a gap in existing datasets for related tasks by incorporating human-generated outputs and their metadata, which is crucial for developing feedback systems that can replicate human-like spontaneous responses.
The CMFeed dataset is comprehensive and includes various modalities such as images, text, human comments, comments’ metadata, and sentiment labels. By leveraging this dataset, the authors define a novel task called “controllable feedback synthesis,” which aims to generate context-aware feedback aligned with the desired sentiment.
To accomplish this task, the authors propose a benchmark feedback synthesis system that consists of encoder, decoder, and controllability modules. The system utilizes transformer and Faster R-CNN networks to extract features from the multimodal inputs and generate sentiment-specific feedback. Notably, the proposed system achieves a sentiment classification accuracy of 77.23%, which is significantly higher (18.82% improvement) compared to models that do not leverage the unique controllability features of the CMFeed dataset.
In addition to the core synthesis system, the authors incorporate a similarity module for relevance assessment using rank-based metrics. This module helps evaluate the generated feedback’s relevance to the input and further enhances the overall performance of the system.
Overall, this paper presents an important contribution to the field of feedback synthesis by introducing the CMFeed dataset and a benchmark system. The utilization of human-generated outputs and their metadata in training models is a significant step towards developing more realistic and context-aware feedback systems. The proposed system’s impressive results demonstrate the effectiveness of leveraging the controllability features provided by the CMFeed dataset. Future research in this area could explore expanding the dataset and benchmark system to include additional modalities or refining the controllability modules for even more precise sentiment alignment.
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