You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere at AWS, we embrace our differences. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. Job summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. To construct core sets we would, ideally, sort embeddings into clusters of equal diameter and select the center point - the centroid - of each cluster. The basic idea is that, in each of the network’s encoders, we preserve the embedding of the CLS token but select a representative subset - a core set - of the other tokens’ embeddings.Įmbeddings are vectors, so they can be interpreted as points in a multidimensional space. That’s the redundancy we’re trying to remove. But its embedding is also very similar to those of all the other tokens in the sentence. By the time the tokens pass through the final encoder, the embedding of the CLS token ends up representing the sentence as a whole (hence the CLS token’s name). It’s because the attention mechanism must compare every word in an input sequence to every other that a BERT model’s memory footprint scales with the square of the input.ĭetermining the optimal architectural parameters reduces network size by 84% while improving performance on natural-language-understanding tasks.Īs tokens pass through the series of encoders, their embeddings factor in more and more information about other tokens in the sequence, since they’re attending to other tokens that are also factoring in more and more information. Each encoder has an attention mechanism, which decides how much each token’s embedding should reflect information carried by other tokens.įor instance, given the sentence “Bob told his brother that he was starting to get on his nerves,” the attention mechanism should pay more attention to the word “Bob” when encoding the word “his” but “brother” when encoding the word “he”. The start of each sentence is demarcated by a special token called - for reasons that will soon be clear - CLS, for classification.Įach token passes through a series of encoders - usually somewhere between four and 12 - each of which produces a new embedding for each input token. Most tokens are words, but some are multiword phrases, some are subword parts, some are individual letters of acronyms, and so on. A token’s progressĮach sentence input to a BERT model is broken into units called tokens. At that compression rate, the best existing approach suffers an accuracy dropoff of 4%. Moreover, when we apply our method to Performers - variations on BERT models that are specifically designed for long texts - we can reduce the models’ memory footprint by 70%, while actually increasing accuracy. We compare Pyramid-BERT to several state-of-the-art techniques for making BERT models more efficient and show that we can speed inference up 3- to 3.5-fold while suffering an accuracy drop of only 1.5%, whereas, at the same speeds, the best existing method loses 2.5% of its accuracy.Ĭombination of distillation and distillation-aware quantization compresses BART model to 1/16th its size. To make BERT-based models more efficient, we progressively eliminate redundant individual-word embeddings in intermediate layers of the network, while trying to minimize the effect on the complete-sentence embeddings. Downstream applications such as text classification and ranking, however, use only the complete-sentence embeddings. The reduced memory footprint also enables BERT models to operate on longer text sequences.īERT-based models take sequences of sentences as inputs and output vector representations - embeddings - of both each sentence as a whole and its constituent words individually. A simplified illustration of the Pyramid-BERT architecture.Īt this year’s meeting of the Association for Computational Linguistics ( ACL), my colleagues and I presented a new method, called Pyramid-BERT, that reduces the training time, inference time, and memory footprint of BERT-based models, without sacrificing much accuracy.
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