Combining Deep Learning and Topic Modeling for Review Understanding in Context-Aware Recommendation (N18-1)
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| Challenge: | Existing models for user reviews are limited by data sparsity and lack of data. |
| Approach: | They propose to integrate LSTM and Topic Modeling to extract review information for recommender systems by utilizing user reviews. |
| Outcome: | The proposed model outperforms existing models on Amazon review dataset and shows better ability on making topic clustering than traditional topic model based method. |
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| Challenge: | Existing methods to learn user and item representations from review texts do not take into account the user-user and item-item relatedness of the user. |
| Approach: | They propose to use review content and user-item graphs to integrate them as different views. |
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Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis (2024.eacl-long)
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Zongxia Li, Andrew Mao, Daniel Stephens, Pranav Goel, Emily Walpole, Alden Dima, Juan Fung, Jordan Boyd-Graber
| Challenge: | Existing evaluation metrics such as coherence and coherency are inadequate for neural topic models. |
| Approach: | They conduct the first evaluation of neural, supervised and classical topic models in an interactive task-based setting. |
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Topic Modeling: Contextual Token Embeddings Are All You Need (2024.findings-emnlp)
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| Challenge: | Current neural approaches to topic modeling have not been able to solve all of the problems. |
| Approach: | They propose a topic modeling approach that uses document contextual token embeddings to find topics and find topic spans within documents. |
| Outcome: | The proposed model outperforms the current state-of-the-art models on a comprehensive set of topic model evaluation metrics. |
Modeling and Prediction of Online Product Review Helpfulness: A Survey (P18-1)
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| Challenge: | review helpfulness modeling is a task that studies the mechanisms that affect review helpfuliness and attempts to accurately predict it. |
| Approach: | This paper provides an overview of the most relevant work in helpfulness prediction . it discusses the insights gained from said work and provides guidelines for future research . |
| Outcome: | This paper summarizes the most relevant work in helpfulness prediction and understanding in the past decade . it outlines the insights gained from the results and provides guidelines for future research . |
A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews (2021.naacl-main)
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| Challenge: | Existing topic models may extract topics associated with writers’ subjective opinions mixed with those related to factual descriptions. |
| Approach: | They propose a neural topic model combined with adversarial training to disentangle opinion topics from plot and neutral ones. |
| Outcome: | The proposed model shows improved coherence and variety of topics, consistent disentanglement rate, and superior sentiment classification performance to other supervised topic models. |
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. |
| Approach: | They propose a dynamic structured neural topic model which captures topic dependencies while capturing their dependencies. |
| Outcome: | The proposed model outperforms a prior dynamic embedded topic model regarding perplexity and coherence while maintaining sufficient diversity across topics. |
Towards Reinterpreting Neural Topic Models via Composite Activations (2022.emnlp-main)
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| Challenge: | Most Neural Topic Models (NTMs) use a variational auto-encoder framework producing K topics limited to the size of the encoder’s output. |
| Approach: | They propose a model-free two-stage process to reinterpret NTM and derive further insights on the state of the trained model. |
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Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews (2021.eacl-main)
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| Challenge: | Existing models for sentiment-topic extraction assume topics are grouped under discrete sentiment categories such as ‘positive’, ‘negative’ and ‘neural’. |
| Approach: | They propose a Brand-Topic Model which aims to detect brand-associated polarity-bearing topics from product reviews. |
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Self-Supervised Neural Topic Modeling (2021.findings-emnlp)
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| Challenge: | Topic models are useful tools for analyzing and interpreting the main underlying themes of large corpora of text. |
| Approach: | They propose a self-supervised neural topic model that learns a topic representation jointly from three co-occurring words and a document that the triple originates from. |
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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. |