Venue
International Conference on Learning Representations
Domain
natural language processing
Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and encode a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.
This paper proposes a novel approach to neural machine translation (NMT) called RNNsearch, which improves upon the encoder-decoder architecture by incorporating a mechanism that allows the model to (soft-)search for parts of the source sentence relevant to each target word being translated. This addresses the bottleneck of using a fixed-length vector in traditional NMT systems. The authors show that this new method can significantly improve translation quality, especially for longer sentences, achieving performance comparable to state-of-the-art phrase-based systems on English-to-French translation. Alignments generated by the model are shown to be intuitive and reliable, allowing for better handling of long input sentences without losing important contextual information. Qualitative evaluations illustrate the model's ability to maintain grammatical correctness and semantic integrity in translations.
This paper employs the following methods:
- Encoder-Decoder
- RNN
- Bidirectional RNN
- RNN Encoder-Decoder
- RNNsearch
The following datasets were used in this research:
- Achieved translation performance comparable to existing state-of-the-art phrase-based systems on English-to-French translation
- Proposed model more robust to long sentences compared to traditional methods
The authors identified the following limitations:
- Number of GPUs: None specified
- GPU Type: None specified
neural machine translation
encoder-decoder
attention mechanism
sequence-to-sequence
alignment