1 code implementation • CL 2016 • I. Beltagy, Stephen Roller, Pengxiang Cheng, Katrin Erk, Raymond J. Mooney
In this paper, we focus on the three components of a practical system integrating logical and distributional models: 1) Parsing and task representation is the logic-based part where input problems are represented in probabilistic logic.
no code implementations • EMNLP 2016 • Stephen Roller, Katrin Erk
We consider the task of predicting lexical entailment using distributional vectors.
no code implementations • IJCNLP 2017 • Su Wang, Stephen Roller, Katrin Erk
We test whether distributional models can do one-shot learning of definitional properties from text only.
1 code implementation • NAACL 2018 • Pengxiang Cheng, Katrin Erk
Implicit arguments are not syntactically connected to their predicates, and are therefore hard to extract.
1 code implementation • NAACL 2018 • Su Wang, Greg Durrett, Katrin Erk
Distributional data tells us that a man can swallow candy, but not that a man can swallow a paintball, since this is never attested.
no code implementations • NAACL 2018 • Alex Rosenfeld, Katrin Erk
This evaluation quantitatively measures how well a model captures the semantic trajectory of a word over time.
no code implementations • EMNLP 2018 • Su Wang, Eric Holgate, Greg Durrett, Katrin Erk
During natural disasters and conflicts, information about what happened is often confusing, messy, and distributed across many sources.
1 code implementation • 8 Nov 2018 • Pengxiang Cheng, Katrin Erk
Implicit arguments, which cannot be detected solely through syntactic cues, make it harder to extract predicate-argument tuples.
1 code implementation • WS 2019 • Elisa Ferracane, Titan Page, Junyi Jessy Li, Katrin Erk
The first step in discourse analysis involves dividing a text into segments.
1 code implementation • ACL 2019 • Elisa Ferracane, Greg Durrett, Junyi Jessy Li, Katrin Erk
Discourse structure is integral to understanding a text and is helpful in many NLP tasks.
no code implementations • IJCNLP 2019 • Su Wang, Greg Durrett, Katrin Erk
The news coverage of events often contains not one but multiple incompatible accounts of what happened.
no code implementations • 11 Nov 2019 • Pengxiang Cheng, Katrin Erk
Recent progress in NLP witnessed the development of large-scale pre-trained language models (GPT, BERT, XLNet, etc.)
no code implementations • 17 Aug 2020 • Su Wang, Greg Durrett, Katrin Erk
We propose a method for controlled narrative/story generation where we are able to guide the model to produce coherent narratives with user-specified target endings by interpolation: for example, we are told that Jim went hiking and at the end Jim needed to be rescued, and we want the model to incrementally generate steps along the way.
1 code implementation • SCiL 2021 • Katrin Erk, Aurelie Herbelot
In this paper, we derive a notion of 'word meaning in context' that characterizes meaning as both intensional and conceptual.
1 code implementation • EMNLP 2020 • Venkata Subrahmanyan Govindarajan, Benjamin T Chen, Rebecca Warholic, Katrin Erk, Junyi Jessy Li
Humans use language to accomplish a wide variety of tasks - asking for and giving advice being one of them.
1 code implementation • CONLL 2020 • Gabriella Chronis, Katrin Erk
This paper investigates contextual language models, which produce token representations, as a resource for lexical semantics at the word or type level.
1 code implementation • COLING 2020 • Yejin Cho, Juan Diego Rodriguez, Yifan Gao, Katrin Erk
We formulate the problem of hypernym prediction as a sequence generation task, where the sequences are taxonomy paths in WordNet.
1 code implementation • NAACL 2021 • Elisa Ferracane, Greg Durrett, Junyi Jessy Li, Katrin Erk
Discourse signals are often implicit, leaving it up to the interpreter to draw the required inferences.
no code implementations • 29 Jun 2022 • Venelin Kovatchev, Trina Chatterjee, Venkata S Govindarajan, Jifan Chen, Eunsol Choi, Gabriella Chronis, Anubrata Das, Katrin Erk, Matthew Lease, Junyi Jessy Li, Yating Wu, Kyle Mahowald
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability.
1 code implementation • 5 Dec 2022 • Sai Vallurupalli, Sayontan Ghosh, Katrin Erk, Niranjan Balasubramanian, Francis Ferraro
Knowledge about outcomes is critical for complex event understanding but is hard to acquire.
1 code implementation • 29 May 2023 • Gabriella Chronis, Kyle Mahowald, Katrin Erk
We study semantic construal in grammatical constructions using large language models.
1 code implementation • 16 Sep 2023 • Juan Diego Rodriguez, Katrin Erk, Greg Durrett
Aligned paragraphs are sourced from Wikipedia pages in different languages, reflecting real information divergences observed in the wild.
no code implementations • 3 Apr 2024 • Katrin Erk, Marianna Apidianaki
We combine seed-based vectors with guidance from human ratings of where words fall along a specific dimension, and evaluate on predicting both object properties like size and danger, and the stylistic properties of formality and complexity.
no code implementations • NAACL (DADC) 2022 • Venelin Kovatchev, Trina Chatterjee, Venkata S Govindarajan, Jifan Chen, Eunsol Choi, Gabriella Chronis, Anubrata Das, Katrin Erk, Matthew Lease, Junyi Jessy Li, Yating Wu, Kyle Mahowald
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability.
no code implementations • IWCS (ACL) 2021 • Eric Holgate, Katrin Erk
What is the best way to learn embeddings for entities, and what can be learned from them?