no code implementations • WMT (EMNLP) 2020 • Lei Yu, Laurent Sartran, Po-Sen Huang, Wojciech Stokowiec, Domenic Donato, Srivatsan Srinivasan, Alek Andreev, Wang Ling, Sona Mokra, Agustin Dal Lago, Yotam Doron, Susannah Young, Phil Blunsom, Chris Dyer
This paper describes the DeepMind submission to the Chinese\rightarrowEnglish constrained data track of the WMT2020 Shared Task on News Translation.
no code implementations • 18 Jul 2022 • Domenic Donato, Lei Yu, Wang Ling, Chris Dyer
We introduce a new distributed policy gradient algorithm and show that it outperforms existing reward-aware training procedures such as REINFORCE, minimum risk training (MRT) and proximal policy optimization (PPO) in terms of training stability and generalization performance when optimizing machine translation models.
no code implementations • ICLR 2022 • Wang Ling, Wojciech Stokowiec, Domenic Donato, Laurent Sartran, Lei Yu, Austin Matthews, Chris Dyer
When applied to autoregressive models, our algorithm has different biases than beam search has, which enables a new analysis of the role of decoding bias in autoregressive models.
no code implementations • ICLR 2020 • Lingpeng Kong, Cyprien de Masson d'Autume, Wang Ling, Lei Yu, Zihang Dai, Dani Yogatama
We show state-of-the-art word representation learning methods maximize an objective function that is a lower bound on the mutual information between different parts of a word sequence (i. e., a sentence).
no code implementations • TACL 2020 • Lei Yu, Laurent Sartran, Wojciech Stokowiec, Wang Ling, Lingpeng Kong, Phil Blunsom, Chris Dyer
We show that Bayes' rule provides an effective mechanism for creating document translation models that can be learned from only parallel sentences and monolingual documents---a compelling benefit as parallel documents are not always available.
no code implementations • 25 Sep 2019 • Lei Yu, Laurent Sartran, Wojciech Stokowiec, Wang Ling, Lingpeng Kong, Phil Blunsom, Chris Dyer
We show that Bayes' rule provides a compelling mechanism for controlling unconditional document language models, using the long-standing challenge of effectively leveraging document context in machine translation.
no code implementations • 25 Sep 2019 • Wang Ling, Chris Dyer, Lei Yu, Lingpeng Kong, Dani Yogatama, Susannah Young
In natural images, transitions between adjacent pixels tend to be smooth and gradual, a fact that has long been exploited in image compression models based on predictive coding.
no code implementations • 31 Jan 2019 • Dani Yogatama, Cyprien de Masson d'Autume, Jerome Connor, Tomas Kocisky, Mike Chrzanowski, Lingpeng Kong, Angeliki Lazaridou, Wang Ling, Lei Yu, Chris Dyer, Phil Blunsom
We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to new tasks quickly.
no code implementations • ICLR 2019 • Lingpeng Kong, Gabor Melis, Wang Ling, Lei Yu, Dani Yogatama
We present a new theoretical perspective of data noising in recurrent neural network language models (Xie et al., 2017).
no code implementations • 26 Nov 2018 • Lei Yu, Cyprien de Masson d'Autume, Chris Dyer, Phil Blunsom, Lingpeng Kong, Wang Ling
The meaning of a sentence is a function of the relations that hold between its words.
no code implementations • ICLR 2018 • Dani Yogatama, Yishu Miao, Gabor Melis, Wang Ling, Adhiguna Kuncoro, Chris Dyer, Phil Blunsom
We compare and analyze sequential, random access, and stack memory architectures for recurrent neural network language models.
no code implementations • ACL 2017 • Wang Ling, Dani Yogatama, Chris Dyer, Phil Blunsom
Solving algebraic word problems requires executing a series of arithmetic operations{---}a program{---}to obtain a final answer.
1 code implementation • 11 May 2017 • Wang Ling, Dani Yogatama, Chris Dyer, Phil Blunsom
Solving algebraic word problems requires executing a series of arithmetic operations---a program---to obtain a final answer.
2 code implementations • 6 Mar 2017 • Dani Yogatama, Chris Dyer, Wang Ling, Phil Blunsom
We empirically characterize the performance of discriminative and generative LSTM models for text classification.
no code implementations • 31 Dec 2016 • Silvio Amir, Rámon Astudillo, Wang Ling, Paula C. Carvalho, Mário J. Silva
This allows us to expand lexicons describing multiple semantic properties.
no code implementations • 28 Nov 2016 • Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling
We use reinforcement learning to learn tree-structured neural networks for computing representations of natural language sentences.
no code implementations • EMNLP 2017 • Zichao Yang, Phil Blunsom, Chris Dyer, Wang Ling
We propose a general class of language models that treat reference as an explicit stochastic latent variable.
Ranked #1 on Recipe Generation on allrecipes.com
no code implementations • EMNLP 2016 • Tomáš Kočiský, Gábor Melis, Edward Grefenstette, Chris Dyer, Wang Ling, Phil Blunsom, Karl Moritz Hermann
We present a novel semi-supervised approach for sequence transduction and apply it to semantic parsing.
no code implementations • NAACL 2016 • Lu Wang, Wang Ling
We study the problem of generating abstractive summaries for opinionated text.
no code implementations • ACL 2016 • Yulia Tsvetkov, Manaal Faruqui, Wang Ling, Brian MacWhinney, Chris Dyer
We use Bayesian optimization to learn curricula for word representation learning, optimizing performance on downstream tasks that depend on the learned representations as features.
2 code implementations • ACL 2016 • Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Andrew Senior, Fumin Wang, Phil Blunsom
Many language generation tasks require the production of text conditioned on both structured and unstructured inputs.
Ranked #10 on Code Generation on Django
no code implementations • 14 Nov 2015 • Wang Ling, Isabel Trancoso, Chris Dyer, Alan W. black
We introduce a neural machine translation model that views the input and output sentences as sequences of characters rather than words.
1 code implementation • EMNLP 2015 • Wang Ling, Tiago Luís, Luís Marujo, Ramón Fernandez Astudillo, Silvio Amir, Chris Dyer, Alan W. black, Isabel Trancoso
We introduce a model for constructing vector representations of words by composing characters using bidirectional LSTMs.
Ranked #4 on Part-Of-Speech Tagging on Penn Treebank
no code implementations • 6 Aug 2015 • Luís Marujo, José Portêlo, Wang Ling, David Martins de Matos, João P. Neto, Anatole Gershman, Jaime Carbonell, Isabel Trancoso, Bhiksha Raj
State-of-the-art extractive multi-document summarization systems are usually designed without any concern about privacy issues, meaning that all documents are open to third parties.
7 code implementations • IJCNLP 2015 • Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, Noah A. Smith
We propose a technique for learning representations of parser states in transition-based dependency parsers.
no code implementations • LREC 2014 • Anabela Barreiro, Johanna Monti, Brigitte Orliac, Susanne Preu{\ss}, Kutz Arrieta, Wang Ling, Fern Batista, o, Isabel Trancoso
This paper presents a systematic human evaluation of translations of English support verb constructions produced by a rule-based machine translation (RBMT) system (OpenLogos) and a statistical machine translation (SMT) system (Google Translate) for five languages: French, German, Italian, Portuguese and Spanish.
no code implementations • LREC 2014 • Shikun Zhang, Wang Ling, Chris Dyer
In this paper, we leverage the existence of dual subtitles as a source of parallel data.
no code implementations • COLING 2012 • Luis Marujo, Wang Ling, Anatole Gershman, Jaime Carbonell, João P. Neto, David Matos
We extend the concept of Named Entities to Named Events - commonly occurring events such as battles and earthquakes.