Robust Text Recovery from Noisy Data for Semantic Communication
Authors: Youcheng Pan, Shuoran Jiang, Ke Zhang, Yongshuai Hou, Yang Xiang


Reviewer paper summaries: The paper discusses text recovery from noisy data with the intent to restore original semantic content. The work proposes a pipeline comprised of a graph encoder and a generator. AGENDA dataset, suited for graph-to-text research, is adapted for semantic communication as AGENDA4SC. To simulate noisy data, the graph entities and relationships are passed through Additive White Gaussian Noise channel. Thereafter, the proposed graph encoder and generator is used to restore the original data. Empirical results show comparable performance with GraphWriter in +3dB SNR setting. Evaluation is also conducted against GPT-4 to assess the suitability of out-of-the-box LLMs for restoration of semantic communication tasks.

# Comments after Author Discussion
Please refer to my official comments on this thread. I retain my score.
Summary of strengths: - Empirical results show comparable performance with GraphWriter in +3dB SNR setting and it significantly outperforms non-fine-tuned GPT-4. Proposed method outperforms GraphWriter especially in lossy settings (Table 2).
- The proposed method is conceptually straight-forward.
Summary of weaknesses: - While the core aim of the paper is to propose a solution for effective restoration of semantic communication through noisy channels, the empirical results do not seem sufficient to establish the claim.
- GraphWriter and non-fine-tuned GPT-4 are used for comparison. It may be argued that LLMs with fine-tuning or even with in-context examples may be able to produce significantly better performance than the evaluated setting.
Comments, suggestions and typos: - Line 119 mentions "lossy and lossless KGs" in context of Figure 2. This may be slightly confusing to the reader as the concepts do not explicitly appear in the figure.
Overall assessment scores: 3