no code implementations • 28 May 2020 • Asia J. Biega, Peter Potash, Hal Daumé III, Fernando Diaz, Michèle Finck
Article 5(1)(c) of the European Union's General Data Protection Regulation (GDPR) requires that "personal data shall be [...] adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed (`data minimisation')".
no code implementations • 13 Aug 2019 • Peter Potash, Kaheer Suleman
We propose a two-agent game wherein a questioner must be able to conjure discerning questions between sentences, incorporate responses from an answerer, and keep track of a hypothesis state.
no code implementations • WS 2019 • Peter Potash, Adam Ferguson, Timothy J. Hazen
We detail the process of extracting topical passages for queries submitted to a search engine, creating annotated sets of passages aligned to different stances on a topic, and assessing argument convincingness of passages using pairwise annotation.
no code implementations • 3 Apr 2019 • Peter Potash
One popular method for quantitatively evaluating the utility of sentence embeddings involves using them in downstream language processing tasks that require sentence representations as input.
no code implementations • IJCNLP 2017 • Peter Potash, Robin Bhattacharya, Anna Rumshisky
In this work, we provide insight into three key aspects related to predicting argument convincingness.
no code implementations • EMNLP 2017 • Peter Potash, Anna Rumshisky
In this paper we introduce a practical first step towards the creation of an automated debate agent: a state-of-the-art recurrent predictive model for predicting debate winners.
no code implementations • WS 2017 • Peter Potash, Alexey Romanov, Anna Rumshisky, Mikhail Gronas
We show that on the task of predicting which side is likely to prefer a given article, a Naive Bayes classifier can record 90. 3{\%} accuracy looking only at domain names of the news sources.
no code implementations • SEMEVAL 2017 • Peter Potash, Alexey Romanov, Anna Rumshisky
This paper describes a new shared task for humor understanding that attempts to eschew the ubiquitous binary approach to humor detection and focus on comparative humor ranking instead.
no code implementations • EMNLP 2017 • Peter Potash, Alexey Romanov, Anna Rumshisky
One of the major goals in automated argumentation mining is to uncover the argument structure present in argumentative text.
no code implementations • 9 Dec 2016 • Peter Potash, Alexey Romanov, Anna Rumshisky
Our best supervised system achieved 63. 7% accuracy, suggesting that this task is much more difficult than comparable humor detection tasks.
no code implementations • WS 2018 • Peter Potash, Alexey Romanov, Anna Rumshisky
The goal of this paper is to develop evaluation methods for one such task, ghostwriting of rap lyrics, and to provide an explicit, quantifiable foundation for the goals and future directions of this task.