Papers with mixture component
Probabilistic FastText for Multi-Sense Word Embeddings (P18-1)
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| Challenge: | Probabilistic FastText model for word embeddings captures word senses, sub-word structure, and uncertainty information. |
| Approach: | They propose a model for word embeddings that captures multiple word senses . they represent each word with a Gaussian mixture density, with each vector representing an n-gram . |
| Outcome: | The proposed model outperforms dictionary-level probabilistic embeddings on word-similarity benchmarks. |
Topic-Guided Variational Auto-Encoder for Text Generation (N19-1)
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Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen, Lawrence Carin
| Challenge: | Experimental results show that our model outperforms its competitors on both unconditional and conditional text generation. |
| Approach: | They propose a topic-guided variational auto-encoder model for text generation that specifies a Gaussian mixture model and a neural topic module to generate sentences under the topic. |
| Outcome: | The proposed model outperforms existing variational auto-encoders on unconditional and conditional text generation, and can generate semantically-meaningful sentences with various topics. |
Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance (2021.tacl-1)
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| Challenge: | Abstractive summarization is a novel method for opinionated texts . it uses a recursive Gaussian mixture to generate topic sentences . |
| Approach: | They propose an unsupervised abstractive summarization method for opinionated texts . they alternate the unimodal Gaussian prior with a recursive Gausssian mixture . |
| Outcome: | The proposed method generates topic sentences with tree-structured topic guidance, which are more informative and cover more input contents than the current model. |