| Challenge: | Automated hashtag annotation plays an important role in content understanding for microblog posts. |
| Approach: | They propose to annotate hashtags with a novel sequence generation framework via viewing the hashtag as a short sequence of words. |
| Outcome: | The proposed model outperforms existing models on two large-scale datasets . it can generate rare and even unseen hashtags, which is not possible with existing models . |
Similar Papers
Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts (N18-1)
Copied to clipboard
| Challenge: | Existing keyphrase extraction methods suffer from data sparsity problem when conducted on short and informal texts. |
| Approach: | They propose a neural keyphrase extraction framework for microblog posts that takes conversation context into account and uses four types of neural encoders to represent conversation context. |
| Outcome: | The proposed framework outperforms state-of-the-art keyphrase extraction methods on Twitter and Weibo datasets. |
Topic-Aware Neural Keyphrase Generation for Social Media Language (P19-1)
Copied to clipboard
| Challenge: | Existing methods to extract words from source posts to form keyphrases do not exploit latent topics. |
| Approach: | They propose a sequence-to-sequence-based neural keyphrase generation framework . it allows absent keyphrases to be created, and it allows joint modeling of latent topic representations . |
| Outcome: | The proposed model outperforms extraction and generation models without exploiting latent topics. |
Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse (N18-1)
Copied to clipboard
| Challenge: | Existing methods for recommendation focus on content of individual posts, but we exploit both context and user content and behavior preferences. |
| Approach: | They propose a method that captures conversational context and user content and behavior preferences. |
| Outcome: | The proposed method outperforms methods that only model content without considering discourse on two Twitter datasets. |
#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods based on latent topics cannot capture user interests and thus can't be used to predict how likely a user will post with a hashtag. |
| Approach: | They propose a personalized topic attention model that captures salient contents to personalize hashtag contexts by predicting how likely a user will post with a hashtag. |
| Outcome: | The proposed model significantly outperforms the state-of-the-art recommendation approach without exploiting latent topics. |
HashSet - A Dataset For Hashtag Segmentation (2022.lrec-1)
Copied to clipboard
| Challenge: | Hashtag segmentation is the task of breaking a hashtag into constituent tokens . hashtags are often written in unique ways, including spelling variations, and special characters. |
| Approach: | They propose a dataset that breaks hashtags into constituent tokens to train and validate models. |
| Outcome: | The proposed dataset provides an alternate set of hashtags to build and validate hashtag segmentation models. |
Generating Hashtags for Short-form Videos with Guided Signals (2023.acl-long)
Copied to clipboard
Tiezheng Yu, Hanchao Yu, Davis Liang, Yuning Mao, Shaoliang Nie, Po-Yao Huang, Madian Khabsa, Pascale Fung, Yi-Chia Wang
| Challenge: | Short-form video hashtag recommendation (SVHR) is a classification or ranking problem that selects hashtags from a set of limited candidates. |
| Approach: | They propose a short-form video hashtag recommendation task that better represents how hashtags are created naturally by retrieving relevant hashtags from a large-scale hashtag pool as extra guidance signals. |
| Outcome: | The proposed model outperforms strong classification baselines on two short-form video datasets and the guidance signals boost the performance by 8.11 and 2.17 absolute ROUGE-1 scores on average. |
Hashtags, Emotions, and Comments: A Large-Scale Dataset to Understand Fine-Grained Social Emotions to Online Topics (2020.emnlp-main)
Copied to clipboard
| Challenge: | A large-scale dataset is collected from Chinese microblog Sina Weibo with over 13 thousand trending topics, emotion votes in 24 fine-grained types from massive participants, and user comments to allow context understanding. |
| Approach: | They use a large-scale dataset from Chinese microblog Sina Weibo to examine readers' responses to online discussion topics. |
| Outcome: | The proposed model outperforms the human model in predicting social emotions in a multilabel classification setting. |
Controllable Conversation Generation with Conversation Structures via Diffusion Models (2023.findings-acl)
Copied to clipboard
| Challenge: | Current generation models fail to effectively utilize rich linguistic and world knowledge to generate coherent long text. |
| Approach: | They propose a conversation generation framework that incorporates human knowledge and conversation structures with both controllability and interpretability for better conversation generation. |
| Outcome: | The proposed framework incorporates human knowledge and conversation structures with both controllability and interpretability for better conversation generation. |
Topic-Guided Self-Introduction Generation for Social Media Users (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on social media use tags to profile users, but we have found that sentence-level self-introductions are more natural and engaging. |
| Approach: | They propose a novel topic-guided encoder-decoder framework that uses a user's tweeting history to generate a short sentence outlining their personal interests. |
| Outcome: | The proposed framework outperforms existing encoder-decoder models on a large-scale Twitter dataset and shows that it is more natural and engaging than previous approaches. |
Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for few-bits hashing cannot be guaranteed due to severe information loss. |
| Approach: | They propose a simple unsupervised neural generative semantic hashing method with a focus on few-bits hash. |
| Outcome: | The proposed method improves on the state-of-the-art methods in few-bits hashing. |