Noam Shazeer


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Do Transformer Modifications Transfer Across Implementations and Applications?
Sharan Narang | Hyung Won Chung | Yi Tay | Liam Fedus | Thibault Fevry | Michael Matena | Karishma Malkan | Noah Fiedel | Noam Shazeer | Zhenzhong Lan | Yanqi Zhou | Wei Li | Nan Ding | Jake Marcus | Adam Roberts | Colin Raffel
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

The research community has proposed copious modifications to the Transformer architecture since it was introduced over three years ago, relatively few of which have seen widespread adoption. In this paper, we comprehensively evaluate many of these modifications in a shared experimental setting that covers most of the common uses of the Transformer in natural language processing. Surprisingly, we find that most modifications do not meaningfully improve performance. Furthermore, most of the Transformer variants we found beneficial were either developed in the same codebase that we used or are relatively minor changes. We conjecture that performance improvements may strongly depend on implementation details and correspondingly make some recommendations for improving the generality of experimental results.


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How Much Knowledge Can You Pack Into the Parameters of a Language Model?
Adam Roberts | Colin Raffel | Noam Shazeer
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any external context or knowledge. We show that this approach scales with model size and performs competitively with open-domain systems that explicitly retrieve answers from an external knowledge source when answering questions. To facilitate reproducibility and future work, we release our code and trained models.


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Corpora Generation for Grammatical Error Correction
Jared Lichtarge | Chris Alberti | Shankar Kumar | Noam Shazeer | Niki Parmar | Simon Tong
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Grammatical Error Correction (GEC) has been recently modeled using the sequence-to-sequence framework. However, unlike sequence transduction problems such as machine translation, GEC suffers from the lack of plentiful parallel data. We describe two approaches for generating large parallel datasets for GEC using publicly available Wikipedia data. The first method extracts source-target pairs from Wikipedia edit histories with minimal filtration heuristics while the second method introduces noise into Wikipedia sentences via round-trip translation through bridge languages. Both strategies yield similar sized parallel corpora containing around 4B tokens. We employ an iterative decoding strategy that is tailored to the loosely supervised nature of our constructed corpora. We demonstrate that neural GEC models trained using either type of corpora give similar performance. Fine-tuning these models on the Lang-8 corpus and ensembling allows us to surpass the state of the art on both the CoNLL ‘14 benchmark and the JFLEG task. We present systematic analysis that compares the two approaches to data generation and highlights the effectiveness of ensembling.


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Tensor2Tensor for Neural Machine Translation
Ashish Vaswani | Samy Bengio | Eugene Brevdo | Francois Chollet | Aidan Gomez | Stephan Gouws | Llion Jones | Łukasz Kaiser | Nal Kalchbrenner | Niki Parmar | Ryan Sepassi | Noam Shazeer | Jakob Uszkoreit
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation
Mia Xu Chen | Orhan Firat | Ankur Bapna | Melvin Johnson | Wolfgang Macherey | George Foster | Llion Jones | Mike Schuster | Noam Shazeer | Niki Parmar | Ashish Vaswani | Jakob Uszkoreit | Lukasz Kaiser | Zhifeng Chen | Yonghui Wu | Macduff Hughes
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The past year has witnessed rapid advances in sequence-to-sequence (seq2seq) modeling for Machine Translation (MT). The classic RNN-based approaches to MT were first out-performed by the convolutional seq2seq model, which was then out-performed by the more recent Transformer model. Each of these new approaches consists of a fundamental architecture accompanied by a set of modeling and training techniques that are in principle applicable to other seq2seq architectures. In this paper, we tease apart the new architectures and their accompanying techniques in two ways. First, we identify several key modeling and training techniques, and apply them to the RNN architecture, yielding a new RNMT+ model that outperforms all of the three fundamental architectures on the benchmark WMT’14 English to French and English to German tasks. Second, we analyze the properties of each fundamental seq2seq architecture and devise new hybrid architectures intended to combine their strengths. Our hybrid models obtain further improvements, outperforming the RNMT+ model on both benchmark datasets.


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Sparse Non-negative Matrix Language Modeling
Joris Pelemans | Noam Shazeer | Ciprian Chelba
Transactions of the Association for Computational Linguistics, Volume 4

We present Sparse Non-negative Matrix (SNM) estimation, a novel probability estimation technique for language modeling that can efficiently incorporate arbitrary features. We evaluate SNM language models on two corpora: the One Billion Word Benchmark and a subset of the LDC English Gigaword corpus. Results show that SNM language models trained with n-gram features are a close match for the well-established Kneser-Ney models. The addition of skip-gram features yields a model that is in the same league as the state-of-the-art recurrent neural network language models, as well as complementary: combining the two modeling techniques yields the best known result on the One Billion Word Benchmark. On the Gigaword corpus further improvements are observed using features that cross sentence boundaries. The computational advantages of SNM estimation over both maximum entropy and neural network estimation are probably its main strength, promising an approach that has large flexibility in combining arbitrary features and yet scales gracefully to large amounts of data.