Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Hoyle, Alexander"'
Autor:
Balepur, Nishant, Shu, Matthew, Hoyle, Alexander, Robey, Alison, Feng, Shi, Goldfarb-Tarrant, Seraphina, Boyd-Graber, Jordan
Keyword mnemonics are memorable explanations that link new terms to simpler keywords. Prior work generates mnemonics for students, but they do not train models using mnemonics students prefer and aid learning. We build SMART, a mnemonic generator tra
Externí odkaz:
http://arxiv.org/abs/2406.15352
Autor:
Schulhoff, Sander, Ilie, Michael, Balepur, Nishant, Kahadze, Konstantine, Liu, Amanda, Si, Chenglei, Li, Yinheng, Gupta, Aayush, Han, HyoJung, Schulhoff, Sevien, Dulepet, Pranav Sandeep, Vidyadhara, Saurav, Ki, Dayeon, Agrawal, Sweta, Pham, Chau, Kroiz, Gerson, Li, Feileen, Tao, Hudson, Srivastava, Ashay, Da Costa, Hevander, Gupta, Saloni, Rogers, Megan L., Goncearenco, Inna, Sarli, Giuseppe, Galynker, Igor, Peskoff, Denis, Carpuat, Marine, White, Jules, Anadkat, Shyamal, Hoyle, Alexander, Resnik, Philip
Generative Artificial Intelligence (GenAI) systems are being increasingly deployed across all parts of industry and research settings. Developers and end users interact with these systems through the use of prompting or prompt engineering. While prom
Externí odkaz:
http://arxiv.org/abs/2406.06608
Topic modeling is a well-established technique for exploring text corpora. Conventional topic models (e.g., LDA) represent topics as bags of words that often require "reading the tea leaves" to interpret; additionally, they offer users minimal contro
Externí odkaz:
http://arxiv.org/abs/2311.01449
When people interpret text, they rely on inferences that go beyond the observed language itself. Inspired by this observation, we introduce a method for the analysis of text that takes implicitly communicated content explicitly into account. We use a
Externí odkaz:
http://arxiv.org/abs/2305.14583
Publikováno v:
Forthcoming in EMNLP 2023
Topic models are used to make sense of large text collections. However, automatically evaluating topic model output and determining the optimal number of topics both have been longstanding challenges, with no effective automated solutions to date. Th
Externí odkaz:
http://arxiv.org/abs/2305.12152
Recently, the relationship between automated and human evaluation of topic models has been called into question. Method developers have staked the efficacy of new topic model variants on automated measures, and their failure to approximate human pref
Externí odkaz:
http://arxiv.org/abs/2210.16162
Autor:
Hoyle, Alexander, Goel, Pranav, Peskov, Denis, Hian-Cheong, Andrew, Boyd-Graber, Jordan, Resnik, Philip
Topic model evaluation, like evaluation of other unsupervised methods, can be contentious. However, the field has coalesced around automated estimates of topic coherence, which rely on the frequency of word co-occurrences in a reference corpus. Conte
Externí odkaz:
http://arxiv.org/abs/2107.02173
Generating text from structured inputs, such as meaning representations or RDF triples, has often involved the use of specialized graph-encoding neural networks. However, recent applications of pretrained transformers to linearizations of graph input
Externí odkaz:
http://arxiv.org/abs/2012.15793
Topic models are often used to identify human-interpretable topics to help make sense of large document collections. We use knowledge distillation to combine the best attributes of probabilistic topic models and pretrained transformers. Our modular m
Externí odkaz:
http://arxiv.org/abs/2010.02377
Studying the ways in which language is gendered has long been an area of interest in sociolinguistics. Studies have explored, for example, the speech of male and female characters in film and the language used to describe male and female politicians.
Externí odkaz:
http://arxiv.org/abs/1906.04760