no code implementations • EMNLP 2021 • Andrew Piper, Richard Jean So, David Bamman
Over the past decade, the field of natural language processing has developed a wide array of computational methods for reasoning about narrative, including summarization, commonsense inference, and event detection.
1 code implementation • NAACL (NUSE) 2021 • Li Lucy, David Bamman
Using topic modeling and lexicon-based word similarity, we find that stories generated by GPT-3 exhibit many known gender stereotypes.
1 code implementation • 27 May 2023 • Sandeep Soni, Amanpreet Sihra, Elizabeth F. Evans, Matthew Wilkens, David Bamman
Tracking characters and locations throughout a story can help improve the understanding of its plot structure.
1 code implementation • 26 May 2023 • Kent K. Chang, Danica Chen, David Bamman
We present a new dataset for studying conversation disentanglement in movies and TV series.
1 code implementation • 28 Apr 2023 • Kent K. Chang, Mackenzie Cramer, Sandeep Soni, David Bamman
In this work, we carry out a data archaeology to infer books that are known to ChatGPT and GPT-4 using a name cloze membership inference query.
1 code implementation • 19 Dec 2022 • Li Lucy, Jesse Dodge, David Bamman, Katherine A. Keith
Scholarly text is often laden with jargon, or specialized language that can facilitate efficient in-group communication within fields but hinder understanding for out-groups.
1 code implementation • 24 Oct 2022 • Sandeep Soni, David Bamman, Jacob Eisenstein
A standard measure of the influence of a research paper is the number of times it is cited.
1 code implementation • 21 Oct 2022 • Li Lucy, Divya Tadimeti, David Bamman
A common paradigm for identifying semantic differences across social and temporal contexts is the use of static word embeddings and their distances.
1 code implementation • 12 Feb 2021 • Li Lucy, David Bamman
Much previous work characterizing language variation across Internet social groups has focused on the types of words used by these groups.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Matthew J{\"o}rke, Jon Gillick, Matthew Sims, David Bamman
We present in this work a method for incorporating global context in long documents when making local decisions in sequence labeling problems like NER.
1 code implementation • 21 Sep 2020 • David Bamman, Patrick J. Burns
We present Latin BERT, a contextual language model for the Latin language, trained on 642. 7 million words from a variety of sources spanning the Classical era to the 21st century.
3 code implementations • EMNLP 2020 • Matthew Sims, David Bamman
We present the task of modeling information propagation in literature, in which we seek to identify pieces of information passing from character A to character B to character C, only given a description of their activity in text.
1 code implementation • 15 Dec 2019 • Jon Gillick, David Bamman
We introduce a new method for generating text, and in particular song lyrics, based on the speech-like acoustic qualities of a given audio file.
3 code implementations • LREC 2020 • David Bamman, Olivia Lewke, Anya Mansoor
We present in this work a new dataset of coreference annotations for works of literature in English, covering 29, 103 mentions in 210, 532 tokens from 100 works of fiction.
2 code implementations • ACL 2019 • Matthew Sims, Jong Ho Park, David Bamman
In this work we present a new dataset of literary events{---}events that are depicted as taking place within the imagined space of a novel.
2 code implementations • NAACL 2019 • David Bamman, Sejal Popat, Sheng Shen
We present a new dataset comprised of 210, 532 tokens evenly drawn from 100 different English-language literary texts annotated for ACE entity categories (person, location, geo-political entity, facility, organization, and vehicle).
no code implementations • 14 May 2019 • Jon Gillick, Adam Roberts, Jesse Engel, Douglas Eck, David Bamman
We explore models for translating abstract musical ideas (scores, rhythms) into expressive performances using Seq2Seq and recurrent Variational Information Bottleneck (VIB) models.
no code implementations • NAACL 2018 • Jon Gillick, David Bamman
This work examines the rhetorical techniques that speakers employ during political campaigns.
no code implementations • WS 2018 • Jon Gillick, David Bamman
Soundtracks play an important role in carrying the story of a film.
no code implementations • EMNLP 2017 • Yi Wu, David Bamman, Stuart Russell
Adversarial training is a mean of regularizing classification algorithms by generating adversarial noise to the training data.
no code implementations • EMNLP 2017 • Lara McConnaughey, Jennifer Dai, David Bamman
We introduce the task of book structure labeling: segmenting and assigning a fixed category (such as Table of Contents, Preface, Index) to the document structure of printed books.
no code implementations • 2 Dec 2015 • Philip Massey, Patrick Xia, David Bamman, Noah A. Smith
We present a dataset of manually annotated relationships between characters in literary texts, in order to support the training and evaluation of automatic methods for relation type prediction in this domain (Makazhanov et al., 2014; Kokkinakis, 2013) and the broader computational analysis of literary character (Elson et al., 2010; Bamman et al., 2014; Vala et al., 2015; Flekova and Gurevych, 2015).
no code implementations • TACL 2014 • David Bamman, Noah A. Smith
We present a method for discovering abstract event classes in biographies, based on a probabilistic latent-variable model.
1 code implementation • WS 2013 • Nathan Schneider, Brendan O'Connor, Naomi Saphra, David Bamman, Manaal Faruqui, Noah A. Smith, Chris Dyer, Jason Baldridge
We introduce a framework for lightweight dependency syntax annotation.
no code implementations • 6 May 2013 • David Bamman, Noah A. Smith
We consider the unsupervised alignment of the full text of a book with a human-written summary.
1 code implementation • 16 Oct 2012 • David Bamman, Jacob Eisenstein, Tyler Schnoebelen
Examining individuals whose language does not match the classifier's model for their gender, we find that they have social networks that include significantly fewer same-gender social connections and that, in general, social network homophily is correlated with the use of same-gender language markers.