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20152023
most citedTransition-Based Dependency Parsing with Stack Long Short-Term Memory

526 citations · 1.2k across the 21 of their papers we have counts for

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Showing 2017Show all

6 papers · 1 filter

cs.CL2017

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…

cs.LG2017

A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models

Kartik Goyal, Graham Neubig, Chris Dyer +1

Beam search is a desirable choice of test-time decoding algorithm for neural sequence models because it potentially avoids search errors made by simpler greedy methods. However, ty…

cs.CL201749 cited

On the State of the Art of Evaluation in Neural Language Models

Gábor Melis, Chris Dyer, Phil Blunsom

Ongoing innovations in recurrent neural network architectures have provided a steady influx of apparently state-of-the-art results on language modelling benchmarks. However, these…

cs.CL201795 cited

Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold

Swabha Swayamdipta, Sam Thomson, Chris Dyer +1

We present a new, efficient frame-semantic parser that labels semantic arguments to FrameNet predicates. Built using an extension to the segmental RNN that emphasizes recall, our b…

cs.LG201727 cited

On-the-fly Operation Batching in Dynamic Computation Graphs

Graham Neubig, Yoav Goldberg, Chris Dyer

Dynamic neural network toolkits such as PyTorch, DyNet, and Chainer offer more flexibility for implementing models that cope with data of varying dimensions and structure, relative…

cs.CL2017

Ontology-Aware Token Embeddings for Prepositional Phrase Attachment

Pradeep Dasigi, Waleed Ammar, Chris Dyer +1

Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Inste…