Search Results for author: Tristan Miller

Found 17 papers, 4 papers with code

A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd

1 code implementation NAACL 2019 Tristan Miller, Maria Sukhareva, Iryna Gurevych

The study of argumentation and the development of argument mining tools depends on the availability of annotated data, which is challenging to obtain in sufficient quantity and quality.

Argument Mining

Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning

1 code implementation3 Aug 2020 Tristan Miller, Erik-Lân Do Dinh, Edwin Simpson, Iryna Gurevych

Most humour processing systems to date make at best discrete, coarse-grained distinctions between the comical and the conventional, yet such notions are better conceptualized as a broad spectrum.

Metaheuristic Approaches to Lexical Substitution and Simplification

no code implementations EACL 2017 Sallam Abualhaija, Tristan Miller, Judith Eckle-Kohler, Iryna Gurevych, Karl-Heinz Zimmermann

In this paper, we propose using metaheuristics{---}in particular, simulated annealing and the new D-Bees algorithm{---}to solve word sense disambiguation as an optimization problem within a knowledge-based lexical substitution system.

Lexical Simplification Machine Translation +4

SemEval-2017 Task 7: Detection and Interpretation of English Puns

no code implementations SEMEVAL 2017 Tristan Miller, Christian Hempelmann, Iryna Gurevych

A pun is a form of wordplay in which a word suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another word, for an intended humorous or rhetorical effect.

Word Sense Disambiguation

WordNet---Wikipedia---Wiktionary: Construction of a Three-way Alignment

no code implementations LREC 2014 Tristan Miller, Iryna Gurevych

The coverage and quality of conceptual information contained in lexical semantic resources is crucial for many tasks in natural language processing.

Machine Translation Question Answering +1

The Punster's Amanuensis: The Proper Place of Humans and Machines in the Translation of Wordplay

no code implementations RANLP 2019 Tristan Miller

The translation of wordplay is one of the most extensively researched problems in translation studies, but it has attracted little attention in the fields of natural language processing and machine translation.

Machine Translation Translation

SemEval-2021 Task 12: Learning with Disagreements

no code implementations SEMEVAL 2021 Alexandra Uma, Tommaso Fornaciari, Anca Dumitrache, Tristan Miller, Jon Chamberlain, Barbara Plank, Edwin Simpson, Massimo Poesio

Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision.

End-to-end style-conditioned poetry generation: What does it take to learn from examples alone?

no code implementations EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 Jörg Wöckener, Thomas Haider, Tristan Miller, The-Khang Nguyen, Thanh Tung Linh Nguyen, Minh Vu Pham, Jonas Belouadi, Steffen Eger

In this work, we design an end-to-end model for poetry generation based on conditioned recurrent neural network (RNN) language models whose goal is to learn stylistic features (poem length, sentiment, alliteration, and rhyming) from examples alone.

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