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Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering ...
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One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets ...
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Fine-Grained Spatial Information Extraction in Radiology as Two-turn Question Answering ...
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The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes ...
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407 |
Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification ...
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408 |
Towards Zero-Shot Knowledge Distillation for Natural Language Processing ...
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BERTAC: Enhancing Transformer-based Language Models with Adversarially Pretrained Convolutional Neural Networks ...
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411 |
SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations ...
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412 |
Automatic Text Evaluation through the Lens of Wasserstein Barycenters ...
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413 |
Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting ...
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PSED: A Dataset for Selecting Emphasis in Presentation Slides ...
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Benchmarking Neural Topic Models: An Empirical Study ...
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Abstract:
Read paper: https://www.aclanthology.org/2021.findings-acl.382 Abstract: Neural topic modeling approach has been attracting much attention recently as it is able to leverage the advantages of both neural networks and probabilistic topic models. Previous works have proposed several models that are based on this framework and obtained impressive experimental results compared to traditional probabilistic models. However, the reported result is not consistent across the works, making them hard for gaining a rigorous assessment of these approaches. This work aims to address this issue by offering an extensive empirical evaluation of typical neural topic models in different aspects using large, diverse datasets as well as a thorough set of metrics. Precisely, we examine the performance of these models in three tasks, namely uncovering cohesive topics, modeling the input documents, and representing them for downstream classification. Our results show that while the neural topic models are better in the first and ...
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Keyword:
Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Neural Network; Semantics
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URL: https://underline.io/lecture/26473-benchmarking-neural-topic-models-an-empirical-study https://dx.doi.org/10.48448/xsdd-dh29
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Combining sentence and table evidence to predict veracity of factual claims using TaPaS and RoBERTa ...
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Meta Distant Transfer Learning for Pre-trained Language Models ...
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Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques ...
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Plot and Rework: Modeling Storylines for Visual Storytelling ...
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SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles ...
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