This research paper explores sequence-to-sequence learning using deep Long Short-Term Memory (LSTM) neural networks.

Here’s the paper by Ilya Sutskever (et al.) we’re discussing today:

https://papers.nips.cc/paper_files/paper/2014/file/a14ac55a4f27472c5d894ec1c3c743d2-Paper.pdf

The authors present an end-to-end approach for machine translation, mapping input sequences (e.g., English sentences) to fixed-dimensional vectors and then decoding them into output sequences (e.g., French translations) using another LSTM. A key finding is that reversing the input sequence significantly improves performance by creating shorter-term dependencies, which facilitates training. Experiments on the WMT-14 English-to-French dataset demonstrate that this method achieves state-of-the-art results, outperforming a phrase-based system and showing resilience to long sentences. The model also learns meaningful sentence representations sensitive to word order.

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This research paper explores sequence-to-sequence learning using deep Long Short-Term Memory (LSTM) neural networks.

The authors present an end-to-end approach for machine translation, mapping input sequences (e.g., English sentences) to fixed-dimensional vectors and then decoding them into output sequences (e.g., French translations) using another LSTM.

A key finding is that reversing the input sequence significantly improves performance by creating shorter-term dependencies, which facilitates training. Experiments on the WMT-14 English-to-French dataset demonstrate that this method achieves state-of-the-art results, outperforming a phrase-based system and showing resilience to long sentences. The model also learns meaningful sentence representations sensitive to word order.

This LSTM model improves upon previous sequence-to-sequence learning methods in several ways.

● The LSTM can handle long sentences by reversing the order of words in the source sentence but not the target sentence. This introduces many short-term dependencies between the source and target sentences, making the optimization problem easier for stochastic gradient descent (SGD). Reversing the source sentences also results in LSTMs with better memory utilization.

● The LSTM learns a fixed-dimensional vector representation of the input sentence. The model uses one LSTM to read the input sequence one timestep at a time to obtain this vector representation. Then, another LSTM extracts the output sequence from the vector. The translation objective encourages the LSTM to find sentence representations that capture their meaning.

● The model uses two different LSTMs, one for the input sequence and one for the output sequence. This increases the number of model parameters, making it possible to train the LSTM on multiple language pairs simultaneously.

● The model uses a deep LSTM with four layers. Deep LSTMs significantly outperformed shallow LSTMs because each additional layer reduces perplexity.

This approach is similar to other work using LSTMs for machine translation. However, this model differs from the work of Cho et al. and Bahdanau et al. because it does not have difficulty on long sentences and does not require an attention mechanism. This is likely due to reversing the order of words in the source sentence. It is also different from the work of Kalchbrenner and Blunsom who mapped sentences to vectors using convolutional neural networks which lose the order of the words.

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