Summary of Explicit Word Density Estimation For Language Modelling, by Jovan Andonov et al.
Explicit Word Density Estimation for Language Modelling
by Jovan Andonov, Octavian Ganea, Paulina Grnarova, Gary Bécigneul, Thomas Hofmann
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to language modeling by combining neural networks with matrix factorization techniques. Specifically, it shows that the Softmax layer in traditional LSTM-based language models imposes an upper bound on the expressiveness of the model, limiting its ability to capture complex linguistic structures. To address this limitation, the authors propose a new family of language models based on NeuralODEs and Normalizing Flows, which enable continuous learning and improved performance. The paper demonstrates the effectiveness of these models on various benchmarks and shows that they can be used to improve upon state-of-the-art results in certain tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving how computers understand language. Right now, most language models are based on a type of neural network called LSTM. But researchers have found that this method has limitations when trying to capture the complexity of human language. To overcome these limitations, the authors propose two new approaches: NeuralODEs and Normalizing Flows. These methods allow for continuous learning and better performance. The paper shows that these new models can be used to improve results in certain tasks, making them more useful for applications like chatbots or language translation. |
Keywords
* Artificial intelligence * Lstm * Neural network * Softmax * Translation
