Document Summary
This paper introduces a novel neural machine translation (NMT) architecture that jointly learns to align and translate, addressing the limitations of fixed-length vector representations in conventional encoder-decoder models. The approach involves an encoder that maps a source sentence into a sequence of vectors and a decoder that emulates searching through the source sentence during translation.
Key Concepts and Contributions
- Fixed-Length Vector Bottleneck: The paper identifies the use of a fixed-length vector to represent the entire source sentence as a bottleneck, especially for long sentences, leading to performance degradation in basic encoder-decoder models.
- Joint Alignment and Translation: The proposed model extends the encoder-decoder architecture by incorporating an alignment mechanism. The model (soft-)searches for relevant parts of the source sentence when predicting each target word. This is done without explicitly forming hard segments.
- Bidirectional RNN Encoder: The encoder uses a bidirectional recurrent neural network (BiRNN) to annotate sequences, capturing both preceding and following context for each word.
- Attention Mechanism: The decoder employs an attention mechanism to focus on different parts of the source sentence when generating each word of the translation. The context vector is computed as a weighted sum of annotations, where weights reflect the importance of each annotation.
- Performance and Analysis: The proposed approach achieves comparable translation performance to the existing state-of-the-art phrase-based system on the English-to-French translation task. Qualitative analysis reveals that the (soft-)alignments found by the model align well with intuition.
Experimental Results
The experiments demonstrate that the proposed RNNsearch model outperforms the conventional RNNencdec model, especially for longer sentences. It achieves performance comparable to the phrase-based translation system (Moses) when considering sentences with only known words.
Related Works
The paper discusses related works in neural networks for machine translation, emphasizing the radical departure of the proposed NMT approach from earlier methods that primarily used neural networks as a component of existing statistical machine translation systems.
This research contributes a new approach to neural machine translation, effectively addressing the limitations of fixed-length vector representations and offering a promising step toward better machine translation and a better understanding of natural languages in general.