Sequence to Sequence Learning with Neural Networks
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.
Preview
Synopsis
Overview
- Keywords: Sequence to Sequence Learning, Neural Networks, LSTM, Machine Translation, BLEU Score
- Objective: Present a novel end-to-end approach for sequence-to-sequence learning using Long Short-Term Memory (LSTM) networks.
- Hypothesis: The proposed LSTM architecture can effectively map variable-length input sequences to variable-length output sequences, outperforming traditional statistical machine translation (SMT) systems.
- Innovation: Introduction of a simple yet effective technique of reversing input sequences to enhance learning and performance on long sentences.
Background
Preliminary Theories:
- Recurrent Neural Networks (RNNs): Generalization of feedforward networks to handle sequential data, but struggle with variable-length input-output mappings.
- Long Short-Term Memory (LSTM): A type of RNN designed to learn long-range dependencies, mitigating issues like vanishing gradients.
- Statistical Machine Translation (SMT): Traditional approach to translation that relies on statistical models and fixed vocabulary, often limited by the need for predefined alignments.
- Attention Mechanisms: Techniques that allow models to focus on specific parts of the input sequence, improving performance on tasks with long-range dependencies.
Prior Research:
- Kalchbrenner and Blunsom (2013): First to map entire input sentences to vectors using convolutional networks.
- Cho et al. (2014): Introduced an RNN encoder-decoder architecture for translation tasks, focusing on integrating neural networks into SMT systems.
- Bahdanau et al. (2014): Developed an attention-based model that improved translation quality by addressing long sentence issues.
Methodology
Key Ideas:
- Two-Layer LSTM Architecture: One LSTM encodes the input sequence into a fixed-dimensional vector, while another decodes this vector into the output sequence.
- Reversing Input Sequences: Input sentences are reversed to create short-term dependencies, simplifying the optimization problem and improving performance.
- End-to-End Training: The model is trained directly on translation tasks without relying on intermediate representations.
Experiments:
- WMT’14 English to French Translation Task: Utilized a dataset of 12 million sentences to evaluate the model's performance.
- BLEU Score Evaluation: Used BLEU scores to measure translation quality, achieving a score of 34.8, surpassing the SMT baseline of 33.3.
- Rescoring of N-Best Lists: The LSTM was also used to rescore hypotheses from an SMT system, achieving a BLEU score of 36.5.
Implications: The methodology allows for flexible handling of variable-length sequences and improves translation quality through innovative data preprocessing.
Findings
Outcomes:
- Performance on Long Sentences: The LSTM model performed well on long sentences, contrary to expectations, due to the reversal of input sequences.
- Improved BLEU Scores: The model achieved competitive BLEU scores, indicating its effectiveness compared to traditional SMT systems.
- Sensitivity to Word Order: The LSTM learned to maintain the order of words, enhancing the quality of translations.
Significance: This research demonstrates that a relatively simple LSTM-based approach can outperform complex SMT systems, highlighting the potential of neural networks in sequence learning tasks.
Future Work: Further exploration of different architectures, attention mechanisms, and larger datasets could enhance performance and applicability to other sequence-based tasks.
Potential Impact: Advancements in sequence-to-sequence learning could lead to significant improvements in machine translation, speech recognition, and other natural language processing applications.