Search Results for author: Peter Potash

Found 14 papers, 0 papers with code

Operationalizing the Legal Principle of Data Minimization for Personalization

no code implementations28 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')".

Recommendation Systems

Playing log(N)-Questions over Sentences

no code implementations13 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.

Ranking Passages for Argument Convincingness

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.

The Effect of Downstream Classification Tasks for Evaluating Sentence Embeddings

no code implementations3 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.

Classification General Classification +1

Towards Debate Automation: a Recurrent Model for Predicting Debate Winners

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.

Text Generation

Tracking Bias in News Sources Using Social Media: the Russia-Ukraine Maidan Crisis of 2013--2014

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.

SemEval-2017 Task 6: \#HashtagWars: Learning a Sense of Humor

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.

Humor Detection

Here's My Point: Joint Pointer Architecture for Argument Mining

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.

Argument Mining

Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting

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.

Text Generation

#HashtagWars: Learning a Sense of Humor

no code implementations9 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.

Humor Detection

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