no code implementations • 14 Oct 2021 • Benjamin Muller, Luca Soldaini, Rik Koncel-Kedziorski, Eric Lind, Alessandro Moschitti
Recent approaches for question answering systems have achieved impressive performance on English by combining document-level retrieval with answer generation.
1 code implementation • NLP4ConvAI (ACL) 2022 • Zhilin Wang, Xuhui Zhou, Rik Koncel-Kedziorski, Alex Marin, Fei Xia
Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes.
no code implementations • ACL 2022 • Yao Dou, Maxwell Forbes, Rik Koncel-Kedziorski, Noah A. Smith, Yejin Choi
To support the broad range of real machine errors that can be identified by laypeople, the ten error categories of Scarecrow -- such as redundancy, commonsense errors, and incoherence -- are identified through several rounds of crowd annotation experiments without a predefined ontology.
1 code implementation • 18 Apr 2021 • Rik Koncel-Kedziorski, Noah A. Smith
This method can improve perplexity of pretrained LMs with no updates to the LM's own parameters.
1 code implementation • 19 Sep 2020 • Zeqiu Wu, Rik Koncel-Kedziorski, Mari Ostendorf, Hannaneh Hajishirzi
Knowledge graphs capture entities and relations from long documents and can facilitate reasoning in many downstream applications.
1 code implementation • 1 May 2020 • Zeqiu Wu, Michel Galley, Chris Brockett, Yizhe Zhang, Xiang Gao, Chris Quirk, Rik Koncel-Kedziorski, Jianfeng Gao, Hannaneh Hajishirzi, Mari Ostendorf, Bill Dolan
Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses.
1 code implementation • ACL 2021 • Kelvin Luu, Xinyi Wu, Rik Koncel-Kedziorski, Kyle Lo, Isabel Cachola, Noah A. Smith
We address the task of explaining relationships between two scientific documents using natural language text.
1 code implementation • ICLR 2020 • Sachin Mehta, Rik Koncel-Kedziorski, Mohammad Rastegari, Hannaneh Hajishirzi
For sequence models with large vocabularies, a majority of network parameters lie in the input and output layers.
1 code implementation • SEMEVAL 2019 • Mark Hopkins, Ronan Le Bras, Cristian Petrescu-Prahova, Gabriel Stanovsky, Hannaneh Hajishirzi, Rik Koncel-Kedziorski
Systems were evaluated based on the percentage of correctly answered questions.
no code implementations • NAACL 2019 • Aida Amini, Saadia Gabriel, Peter Lin, Rik Koncel-Kedziorski, Yejin Choi, Hannaneh Hajishirzi
We introduce a new representation language to model precise operation programs corresponding to each math problem that aim to improve both the performance and the interpretability of the learned models.
3 code implementations • NAACL 2019 • Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, Hannaneh Hajishirzi
Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce.
Ranked #6 on
KG-to-Text Generation
on AGENDA
2 code implementations • EMNLP 2018 • Sachin Mehta, Rik Koncel-Kedziorski, Mohammad Rastegari, Hannaneh Hajishirzi
We introduce the Pyramidal Recurrent Unit (PRU), which enables learning representations in high dimensional space with more generalization power and fewer parameters.
no code implementations • 28 Apr 2018 • Benjamin Robaidek, Rik Koncel-Kedziorski, Hannaneh Hajishirzi
We explore contemporary, data-driven techniques for solving math word problems over recent large-scale datasets.
no code implementations • EMNLP 2016 • Rik Koncel-Kedziorski, Ioannis Konstas, Luke Zettlemoyer, Hannaneh Hajishirzi
Texts present coherent stories that have a particular theme or overall setting, for example science fiction or western.
no code implementations • TACL 2015 • Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, Siena Dumas Ang
This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees.