49 citations · 52 across the 3 of their papers we have counts for
7 papers · 1 filter
Capturing document context inside sentence-level neural machine translation models with self-training
Elman Mansimov, Gábor Melis, Lei Yu
Neural machine translation (NMT) has arguably achieved human level parity when trained and evaluated at the sentence-level. Document-level neural machine translation has received l…
A Critical Analysis of Biased Parsers in Unsupervised Parsing
Chris Dyer, Gábor Melis, Phil Blunsom
A series of recent papers has used a parsing algorithm due to Shen et al. (2018) to recover phrase-structure trees based on proxies for "syntactic depth." These proxy depths are ob…
Unsupervised Recurrent Neural Network Grammars
Yoon Kim, Alexander M. Rush, Lei Yu +3
Recurrent neural network grammars (RNNG) are generative models of language which jointly model syntax and surface structure by incrementally generating a syntax tree and sentence i…
Variational Smoothing in Recurrent Neural Network Language Models
Lingpeng Kong, Gabor Melis, Wang Ling +2
We present a new theoretical perspective of data noising in recurrent neural network language models (Xie et al., 2017). We show that each variant of data noising is an instance of…
Encoding Spatial Relations from Natural Language
Tiago Ramalho, Tomáš Kočiský, Frederic Besse +5
Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fa…
The NarrativeQA Reading Comprehension Challenge
Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom +4
Reading comprehension (RC)---in contrast to information retrieval---requires integrating information and reasoning about events, entities, and their relations across a full documen…