Sequence to Sequence Learning with Neural Networks (1409.3215v3)

Published 10 Sep 2014 in cs.CL and cs.LG

Abstract: Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT'14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous best result on this task. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.

Authors (3)

Ilya Sutskever (58 papers)
Oriol Vinyals (116 papers)
Quoc V. Le (128 papers)

Citations (19,788)

View on Semantic Scholar

Summary

Sequence to Sequence Learning with Neural Networks

The paper "Sequence to Sequence Learning with Neural Networks" by Ilya Sutskever, Oriol Vinyals, and Quoc V. Le explores a generalized approach to sequence learning using Deep Neural Networks (DNNs), with a primary focus on Long Short-Term Memory (LSTM) architectures. This research addresses the limitation of standard DNNs in handling variable-length sequence mappings, a crucial requirement in various domains such as machine translation and speech recognition.

Methodology

The authors propose an end-to-end method that utilizes a deep LSTM to map input sequences to fixed-dimensional vectors and then decodes these vectors back into target sequences using another deep LSTM. This two-fold approach leverages the intrinsic capability of LSTMs to manage long-range temporal dependencies, proving critical for tasks where the time lag between inputs and outputs is substantial.

Key technical contributions include:

LSTM Architecture: The core model employs multilayered LSTM networks, with an encoder LSTM transforming input sequences into fixed-dimensional vectors and a decoder LSTM generating sequences from these vectors.
Sequence Reversal: A notable method involved reversing the order of words in source sequences during training. This trick significantly improved performance by introducing short-term dependencies, thus simplifying the optimization process.
Parallelization: The implementation was optimized for computational efficiency by distributing LSTM layers across multiple GPUs, enhancing training speed.

Experimental Results

The approach was tested on the WMT'14 English to French translation task, yielding a BLEU score of 34.8 using a simple beam-search decoder. This score was enhanced to 36.5 when the LSTM models were employed to rerank the 1000-best list hypotheses generated by a phrase-based Statistical Machine Translation (SMT) system. Notably, the LSTM-driven results outperformed the baseline SMT system score of 33.3.

Implications and Future Directions

The findings underscore the capability of LSTMs to deal with sequence to sequence tasks without extensive domain-specific engineering. The method's relative insensitivity to sentence length challenges previously held views about LSTM's limitations with long sequences. This opens avenues for applying similar models to other sequence learning problems like speech synthesis, video captioning, and more.

Practical implications indicate a shift towards neural network-based models for tasks traditionally dominated by statistical methods. The theoretical contributions include insights into the architectural and data preprocessing choices that can significantly enhance model performance.

Future research directions may involve further optimization of LSTM architectures, exploring alternative RNN configurations, and investigating attention mechanisms to handle dependencies explicitly. Another promising area is the integration of unsupervised learning techniques to improve the handling of out-of-vocabulary words and enrich the model's semantic understanding.

Conclusion

The paper demonstrates that a well-structured deep LSTM can substantially outperform traditional SMT systems in sequence learning tasks, even with relatively straightforward and unoptimized implementations. The research suggests that future efforts in this area can substantially benefit from focusing on data representation strategies and leveraging modern computational resources for model training.