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
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.
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 • 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 • 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.
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.
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.
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.
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.
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.
no code implementations • 14 Oct 2021 • Benjamin Muller, Luca Soldaini, Rik Koncel-Kedziorski, Eric Lind, Alessandro Moschitti
Our cross-lingual generative system outperforms answer sentence selection baselines for all 5 languages and monolingual generative pipelines for three out of five languages studied.
no code implementations • 23 Oct 2022 • Matteo Gabburo, Rik Koncel-Kedziorski, Siddhant Garg, Luca Soldaini, Alessandro Moschitti
In this paper, we propose to train a GenQA model by transferring knowledge from a trained AS2 model, to overcome the aforementioned issue.
no code implementations • 24 May 2023 • Matteo Gabburo, Siddhant Garg, Rik Koncel-Kedziorski, Alessandro Moschitti
Recent studies show that sentence-level extractive QA, i. e., based on Answer Sentence Selection (AS2), is outperformed by Generation-based QA (GenQA) models, which generate answers using the top-k answer sentences ranked by AS2 models (a la retrieval-augmented generation style).
no code implementations • 11 Nov 2023 • Rik Koncel-Kedziorski, Michael Krumdick, Viet Lai, Varshini Reddy, Charles Lovering, Chris Tanner
We demonstrate that the current bottleneck in performance is due to LLMs' limited business and financial understanding, highlighting the value of a challenging benchmark for quantitative reasoning within this domain.
no code implementations • 12 Jan 2024 • Varshini Reddy, Rik Koncel-Kedziorski, Viet Dac Lai, Michael Krumdick, Charles Lovering, Chris Tanner
For large language models (LLMs) to be effective in the financial domain -- where each decision can have a significant impact -- it is necessary to investigate realistic tasks and data.