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What is Non-autoregressive models
Non-Autoregressive Models: Solving the Seq2Seq Problems
In the field of natural language processing, machine translation is one of the most important problems. Sequence-to-sequence (Seq2Seq) models have been very promising in solving these problems. The idea behind Seq2Seq is to use two recurrent neural networks (RNN) to translate one sequence into another. However, the biggest limitation of traditional Seq2Seq models is that they are autoregressive models, which means they generate the output sequence one token at a time. This approach is very slow and computationally expensive. To overcome this limitation, non-autoregressive models have been introduced.
Non-autoregressive models generate all tokens in the output sequence at once, making them much faster than autoregressive models. These models have potential benefits that could reduce the training and inference time required to generate output sequences. This article dives deeper into non-autoregressive models and explains their working and development over time.
The Working of Seq2Seq Models
Sequence to sequence models works on the basis of encoder-decoder architecture. It means the input sequence is encoded to learn meaningful features, and the decoder is used to decode those features to generate the output sequence. Let’s explain this architecture with an example of machine translation.
Let’s assume that for machine translation, our input is in English and output is in French. Therefore, the input sequence would be English words and output sequence would be French words. The encoder would learn the features of the English language and the decoder would use those features to generate the corresponding French sentence.
In traditional Seq2Seq models, the decoder feeds its output at each time step as input to the next time step of the decoder. This means that Seq2Seq models have a chain dependency between tokens, which can greatly slow down the translation process.
Non-autoregressive models, on the other hand, use multiple decoders to generate the output sequence all at once, without the chain dependency between tokens. These models allow parallel processing of the input sequence, thereby significantly reducing the computational complexity.
Non-Autoregressive Models Development
Non-autoregressive models have been developed over the years to improve the speed and performance of machine translation models. This development has focused on removing the dependency between tokens in the decoder. Here are some of the significant methods that have been introduced to generate parallel outputs:
- Mask-Predict: This method involves masking the output sequence so that each token can be predicted independently, thereby removing the chain dependency between tokens. In this method, a matrix is used to mask the output sequence. Mask-Predict method has a low complexity but the quality of output generated is not very good.
- Generative Adversarial Network (GAN): GAN is a popular and powerful method used in Non-Autoregressive models. It has two models: the generator and the discriminator. The generator generates the output sequence and the discriminator differentiates between the real and generated sequence. The generator is trained to generate the output sequence which the discriminator is unable to differentiate from the real sequence. This method has shown good performance and generated high-quality output sequences.
- Auto-regressive Weak Supervision: This approach uses auto-regressive pre-training on a supervised task before training on the actual non-autoregressive objective. This method works well on speech data, image captioning, and natural language processing. It has an edge over other methods that it is flexible enough to handle other inputs other than textual data.
Challenges of Non-Autoregressive Models
Non-autoregressive models have shown great potential in solving long-standing Seq2Seq problems but it still has a few challenges to overcome:
- Quality of Output: Generating high-quality output is still a challenge for non-autoregressive models. Though there has been significant improvement in the output quality of these models over the years, it is still an area where work needs to be done.
- Limited Vocabulary: Non-autoregressive models work on fixed length decoding. Hence, the vocabulary size is limited, which means that it can only generate words that are in its vocabulary. This limitation is not present in autoregressive models which means it can better generate the output sequence.
- Longer Sequences: Non-autoregressive models are slower than the traditional autoregressive models when working on longer sequences. The reason for this is that it needs to generate the entire sequence with all tokens at once.
Conclusion
Non-autoregressive models are a significant improvement over traditional Seq2Seq models. They have achieved great success in solving machine translation problems by generating the output sequence all at once. Though the field of non-autoregressive models is still developing, it is definitely an approach to consider while working on Seq2Seq problems. With the use of parallel processing and masking-predict methods, the speed of non-autoregressive models is increasing. There is a need for more improvement in terms of output quality and limited vocabulary, but with time, these challenges can also be overcome.