Topic-Aware Neural Keyphrase Generation for Social Media Language
Table 6: Comparison results of our ablation models on three datasets (SE: StackExchange) — separate train: our model with pre-trained latent topics; w/o topic-attn: decoder attention without topics (Eq. 7); w/o topic-state: decoder hidden states without topics (Eq. 5). We report F1@1 for Twitter and Weibo, F1@3 for StackExchange. Best results are in bold.
['[BOLD] Model', '[BOLD] Twitter', '[BOLD] Weibo', '[BOLD] SE']
We compare the results of our full model and its four ablated variants to analyze the relative contributions of topics on different components. We also observe that pre-trained topics only bring a small boost, indicated by the close scores yielded by our model (separate train) and Seq2Seq-Copy. This suggests that the joint training is crucial to better absorb latent topics.