119 citations · 219 across the 8 of their papers we have counts for
8 papers
Extracting Summary Knowledge Graphs from Long Documents
Zeqiu Wu, Rik Koncel-Kedziorski, Mari Ostendorf +1
Knowledge graphs capture entities and relations from long documents and can facilitate reasoning in many downstream applications. Extracting compact knowledge graphs containing onl…
A Controllable Model of Grounded Response Generation
Zeqiu Wu, Michel Galley, Chris Brockett +8
Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting respon…
DeFINE: DEep Factorized INput Token Embeddings for Neural Sequence Modeling
Sachin Mehta, Rik Koncel-Kedziorski, Mohammad Rastegari +1
For sequence models with large vocabularies, a majority of network parameters lie in the input and output layers. In this work, we describe a new method, DeFINE, for learning deep…
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
Aida Amini, Saadia Gabriel, Peter Lin +3
We introduce a large-scale dataset of math word problems and an interpretable neural math problem solver that learns to map problems to operation programs. Due to annotation challe…
Text Generation from Knowledge Graphs with Graph Transformers
Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan +2
Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohib…
Pyramidal Recurrent Unit for Language Modeling
Sachin Mehta, Rik Koncel-Kedziorski, Mohammad Rastegari +1
LSTMs are powerful tools for modeling contextual information, as evidenced by their success at the task of language modeling. However, modeling contexts in very high dimensional sp…
Data-Driven Methods for Solving Algebra Word Problems
Benjamin Robaidek, Rik Koncel-Kedziorski, Hannaneh Hajishirzi
We explore contemporary, data-driven techniques for solving math word problems over recent large-scale datasets. We show that well-tuned neural equation classifiers can outperform…
A Theme-Rewriting Approach for Generating Algebra Word Problems
Rik Koncel-Kedziorski, Ioannis Konstas, Luke Zettlemoyer +1
Texts present coherent stories that have a particular theme or overall setting, for example science fiction or western. In this paper, we present a text generation method called {\…