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.
no code implementations • 21 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.
no code implementations • WS 2020 • Agnieszka Falenska, Anders Bj{\"o}rkelund, Jonas Kuhn
Graph-based and transition-based dependency parsers used to have different strengths and weaknesses.
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.
no code implementations • WS 2019 • Xiang Yu, Agnieszka Falenska, Marina Haid, Ngoc Thang Vu, Jonas Kuhn
We introduce the IMS contribution to the Surface Realization Shared Task 2019.
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.
no code implementations • ACL 2019 • Agnieszka Falenska, Jonas Kuhn
Classical non-neural dependency parsers put considerable effort on the design of feature functions.
no code implementations • 7 Nov 2018 • Agnieszka Falenska, Anders Björkelund, Xiang Yu, Jonas Kuhn
In this paper we show which components of the system were the most responsible for its final performance.
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.
no code implementations • CONLL 2017 • Anders Bj{\"o}rkelund, Agnieszka Falenska, Xiang Yu, Jonas Kuhn
This paper presents the IMS contribution to the CoNLL 2017 Shared Task.