Beginner Explanation
Imagine you’re trying to predict the next word in a sentence, like finishing a friend’s story. A nonlinear recurrent language model is like a very smart friend who remembers the entire story, not just the last word. Instead of just looking at the last few words, it uses its memory (like a notebook) to understand the whole context and makes predictions based on that. The ‘nonlinear’ part means it can handle complex patterns in the story, making it better at guessing what comes next, even if the story takes unexpected turns.Technical Explanation
Nonlinear Recurrent Language Models (RNNs) use recurrent neural networks with nonlinear activation functions (like tanh or ReLU) to process sequences of text. Unlike traditional models that may only consider the last few words, RNNs maintain a hidden state that captures information from previous inputs over time. This allows them to learn dependencies in language data. For example, using PyTorch, we can define a simple RNN as follows: “`python import torch import torch.nn as nn class SimpleRNN(nn.Module): def __init__(self, input_size, hidden_size, output_size): super(SimpleRNN, self).__init__() self.rnn = nn.RNN(input_size, hidden_size, nonlinearity=’tanh’) self.fc = nn.Linear(hidden_size, output_size) def forward(self, x): out, _ = self.rnn(x) out = self.fc(out[-1]) # Take the last output return out “` This model can be trained on text data to predict the next word based on the context captured in its hidden state.Academic Context
Nonlinear Recurrent Language Models leverage the principles of recurrent neural networks (RNNs) established by Elman (1990) and further developed with Long Short-Term Memory (LSTM) networks by Hochreiter and Schmidhuber (1997). The nonlinear activation functions allow the model to capture complex relationships within the data, enhancing its predictive capabilities. Key papers include ‘Learning to Generate Sequences’ by Sutskever et al. (2014) and ‘Sequence to Sequence Learning with Neural Networks’ by Cho et al. (2014), which introduced architectures that have become foundational in natural language processing.Code Examples
Example 1:
import torch
import torch.nn as nn
class SimpleRNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(SimpleRNN, self).__init__()
self.rnn = nn.RNN(input_size, hidden_size, nonlinearity='tanh')
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
out, _ = self.rnn(x)
out = self.fc(out[-1]) # Take the last output
return outExample 2:
def __init__(self, input_size, hidden_size, output_size):
super(SimpleRNN, self).__init__()
self.rnn = nn.RNN(input_size, hidden_size, nonlinearity='tanh')
self.fc = nn.Linear(hidden_size, output_size)Example 3:
def forward(self, x):
out, _ = self.rnn(x)
out = self.fc(out[-1]) # Take the last output
return outExample 4:
import torch
import torch.nn as nn
class SimpleRNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):Example 5:
import torch.nn as nn
class SimpleRNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(SimpleRNN, self).__init__()Example 6:
class SimpleRNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(SimpleRNN, self).__init__()
self.rnn = nn.RNN(input_size, hidden_size, nonlinearity='tanh')
self.fc = nn.Linear(hidden_size, output_size)Example 7:
def __init__(self, input_size, hidden_size, output_size):
super(SimpleRNN, self).__init__()
self.rnn = nn.RNN(input_size, hidden_size, nonlinearity='tanh')
self.fc = nn.Linear(hidden_size, output_size)
Example 8:
def forward(self, x):
out, _ = self.rnn(x)
out = self.fc(out[-1]) # Take the last output
return out
```View Source: https://arxiv.org/abs/2511.16652v1