| Challenge: | Neural network language models have played a central role in recent natural language processing advances. |
| Approach: | They propose a recurrent neural network language model that combines probability distributions computed from a final RNN layer and also middle layers. |
| Outcome: | The proposed language model achieves the best score on the Penn Treebank and WikiText-2 benchmark datasets. |
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On Efficiently Representing Regular Languages as RNNs (2024.findings-acl)
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| Challenge: | Recent work by Hewitt et al. (2020) provides an interpretation of the empirical success of recurrent neural networks (RNNs) as language models (LMs). |
| Approach: | They generalize their construction and show that RNNs can efficiently represent a larger class of LMs than previously claimed. |
| Outcome: | The results suggest that RNNs can represent a larger class of LMs than previously claimed . |
Dependency-based Mixture Language Models (2022.acl-long)
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| Challenge: | Existing models to incorporate syntactic structures into neural language models have relied heavily on elaborate components for a specific language model, which makes them unwieldy in practice to fit into other models. |
| Approach: | They propose a dependency-based mixture language model that incorporates syntactic structures into neural language models by mixing previous dependency modeling probabilities with self-attention. |
| Outcome: | The proposed method can be easily and effectively applied to different neural language models while improving neural text generation on various tasks. |
Language Modeling with a General Second-Order RNN (2020.lrec-1)
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| Challenge: | a number of RNNs update their state as the input sequence is processed . second-order RNN architectures show promising performance in language modeling . |
| Approach: | They propose a second-order RNN architecture that generalizes existing ones . they use a Penn Treebank dataset to analyze how their different components affect performance . |
| Outcome: | The proposed architecture generalizes existing RNNs on a Penn Treebank dataset . it shows that removing the first-order terms does not hinder performance . |
How to represent a word and predict it, too: Improving tied architectures for language modelling (D18-1)
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| Challenge: | Recent state-of-the-art models use word embeddings as input and output mappings instead of tied models. |
| Approach: | They propose to decouple hidden state from word embedding prediction . they extend their proposed modification to word2vec models . |
| Outcome: | The proposed architectures achieve comparable or better results compared to previous models without tying . the proposed architecture reduces parameters, enabling more compact models and faster learning. |
Recurrent Neural Language Models as Probabilistic Finite-state Automata (2023.emnlp-main)
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| Challenge: | Existing studies have focused on the expressive power of recurrent neural network LMs to recognize unweighted formal languages. |
| Approach: | They propose to model a strict subset of probabilistic finite-state automata with RNNs . they show that an RNN requires left(N ||right) neurons to represent an LM . |
| Outcome: | The proposed language models can represent a strict subset of probabilistic distributions expressed by finite-state models. |
The Importance of Being Recurrent for Modeling Hierarchical Structure (D18-1)
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| Challenge: | Recent work shows that recurrent neural networks can implicitly capture hierarchical information when trained to solve common natural language processing tasks. |
| Approach: | They propose a convolutional sequence-to-sequence model that exploits hierarchical information implicitly. |
| Outcome: | The proposed model is recurrent and non-recurrent, and it can model hierarchical structure implicitly. |
The Importance of Generation Order in Language Modeling (D18-1)
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| Challenge: | Neural language models are universally autoregressive, generating sentences one token at a time from left to right. |
| Approach: | They propose a two-pass language model that generates partially-filled sentences and fills in missing tokens. |
| Outcome: | The proposed model produces partially-filled sentences and fills in missing tokens. |
Revisiting Simple Neural Probabilistic Language Models (2021.naacl-main)
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| Challenge: | Recent advances in language modeling have been driven not only by advances in neural architectures, but also through hardware and optimization improvements. |
| Approach: | They revisit the neural probabilistic language model (NPLM) of Bengio et al. (2003) which simply concatenates word embeddings within a fixed window and passes the result through a feed-forward network to predict the next word. |
| Outcome: | The proposed model performs better on word-level language model benchmarks than a baseline Transformer with short input contexts but struggles to handle long-term dependencies. |
Simple and Effective Noisy Channel Modeling for Neural Machine Translation (D19-1)
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| Challenge: | Previous work on noisy channel modeling relied on latent variable models that incrementally process the source and target sentence. |
| Approach: | They propose to use a standard sequence to sequence model which utilizes the entire source and target sentences to estimate posterior probability of a target sequence y given a source sequence x. |
| Outcome: | The proposed model outperforms direct models on German-English translations by up to 3.2 BLEU on four language pairs. |
Code-switched Language Models Using Dual RNNs and Same-Source Pretraining (D18-1)
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| Challenge: | Using recurrent neural networks to build language models for code-switched text is an important problem with implications to downstream applications such as speech recognition and machine translation. |
| Approach: | They propose a novel recurrent neural network unit with dual components that focus on each language in the code-switched text separately and a generative model estimated using the training data. |
| Outcome: | The proposed techniques yield significant reductions in perplexity on Mandarin-English task and improve on baseline models. |