Microblog Hashtag Generation via Encoding Conversation Contexts
Table 4: Comparison results on Twitter and Weibo datasets (in %). RG-1 and RG-4 refer to ROUGE-1 and ROUGE-SU4 respectively. The best results in each column are in bold. The “*” after numbers indicates significantly better results than all the other models (p<0.05, paired t-test). Higher values indicate better performance.
['[BOLD] Model', '[BOLD] Twitter F1@1', '[BOLD] Twitter F1@5', '[BOLD] Twitter MAP', '[BOLD] Twitter RG-1', '[BOLD] Twitter RG-4', '[BOLD] Weibo F1@1', '[BOLD] Weibo F1@5', '[BOLD] Weibo MAP', '[BOLD] Weibo RG-1', '[BOLD] Weibo RG-4']
For Classifier, their outputs are ranked according to the logits after a softmax layer. For Extractor, it is unable to produce ranked hashtags and thus no results are reported for F1@5 and MAP. For LDA, as it cannot generate bigram hashtags, no results are presented for ROUGE-SU4. In general, we have the following observations: Here, we present a thorough study to compare hashtag classification and generation. Four models are selected for comparison: two classifiers, Classifier (post only) and Classifier (post+conv), and two sequence generation models, Seq2Seq and Our model. Below, we explore how they perform to predict rare and new hashtags. Our work is also closely related to neural language generation, where the encoder-decoder framework Sutskever et al. In particular, we are inspired by the keyphrase generation studies for scientific articles Meng et al. incorporating word extraction and generation using a seq2seq model with copy mechanism. However, our hashtag generation task is inherently different from theirs. The reason mostly lies in the informal language style of microblog users in writing both target posts and their hashtags. To adapt our model on microblog data, we explore the effects of conversation contexts on hashtag generation, which has never been studied in any prior work before.