Search Results for author: Agnieszka Falenska

Found 13 papers, 1 papers with code

Assessing Gender Bias in Wikipedia: Inequalities in Article Titles

1 code implementation ACL (GeBNLP) 2021 Agnieszka Falenska, Özlem Çetinoğlu

Potential gender biases existing in Wikipedia’s content can contribute to biased behaviors in a variety of downstream NLP systems.

How-to Guides for Specific Audiences: A Corpus and Initial Findings

no code implementations21 Sep 2023 Nicola Fanton, Agnieszka Falenska, Michael Roth

Instructional texts for specific target groups should ideally take into account the prior knowledge and needs of the readers in order to guide them efficiently to their desired goals.

GRAIN-S: Manually Annotated Syntax for German Interviews

no code implementations LREC 2020 Agnieszka Falenska, Zolt{\'a}n Czesznak, Kerstin Jung, Moritz V{\"o}lkel, Wolfgang Seeker, Jonas Kuhn

The dataset extends an existing corpus GRAIN and comes with constituency and dependency trees for six interviews.

Head-First Linearization with Tree-Structured Representation

no code implementations WS 2019 Xiang Yu, Agnieszka Falenska, Ngoc Thang Vu, Jonas Kuhn

We present a dependency tree linearization model with two novel components: (1) a tree-structured encoder based on bidirectional Tree-LSTM that propagates information first bottom-up then top-down, which allows each token to access information from the entire tree; and (2) a linguistically motivated head-first decoder that emphasizes the central role of the head and linearizes the subtree by incrementally attaching the dependents on both sides of the head.

The (Non-)Utility of Structural Features in BiLSTM-based Dependency Parsers

no code implementations ACL 2019 Agnieszka Falenska, Jonas Kuhn

Classical non-neural dependency parsers put considerable effort on the design of feature functions.

Lexicalized vs. Delexicalized Parsing in Low-Resource Scenarios

no code implementations WS 2017 Agnieszka Falenska, {\"O}zlem {\c{C}}etino{\u{g}}lu

We present a systematic analysis of lexicalized vs. delexicalized parsing in low-resource scenarios, and propose a methodology to choose one method over another under certain conditions.

Dependency Parsing Word Embeddings

Cannot find the paper you are looking for? You can Submit a new open access paper.