Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture (N19-1)
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| Challenge: | Unlike Community Question Answering, where questions are mostly factoid based, forum threads are often open-ended and contain repetitive or irrelevant posts. |
| Approach: | They propose a recurrent neural network-based architecture to model the relevance of a post regarding the original post starting the thread and the novelty it brings to the discussion. |
| Outcome: | The proposed model outperforms the state-of-the-art models for text classification on different types of online forum datasets. |
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| Challenge: | Neural topic modeling has been attracting much attention recently due to its ability to leverage the advantages of both neural networks and probabilistic topic models. |
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| Challenge: | Referring Expression Generation models typically rely on features such as salience and grammatical function to make decisions about form and content. |
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Dynamic Structured Neural Topic Model with Self-Attention Mechanism (2023.findings-acl)
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| Challenge: | Recent topic models that capture the time-series evolution of topics assume that topics evolve independently without interaction. |
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Predicting Reference: What do Language Models Learn about Discourse Models? (2020.emnlp-main)
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| Challenge: | a growing literature that probes neural language models to assess their latent acquisition of grammatical knowledge has not investigated their acquisition of discourse modeling ability. |
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Neural Topic Modeling with Cycle-Consistent Adversarial Training (2020.emnlp-main)
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| Challenge: | Recent advances on deep generative models have attracted significant interest in neural topic modeling. |
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| Challenge: | Existing models for user reviews are limited by data sparsity and lack of data. |
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Re-entry Prediction for Online Conversations via Self-Supervised Learning (2021.findings-emnlp)
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| Challenge: | Existing work on re-entry prediction ignores conversation thread patterns and repeated engagement of target users. |
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