no code implementations • NAACL (CMCL) 2021 • Qi Yu, Aikaterini-Lida Kalouli, Diego Frassinelli
This paper describes the submission of the team KonTra to the CMCL 2021 Shared Task on eye-tracking prediction.
1 code implementation • IWCS (ACL) 2021 • Aikaterini-Lida Kalouli, Rebecca Kehlbeck, Rita Sevastjanova, Oliver Deussen, Daniel Keim, Miriam Butt
Research in NLP has mainly focused on factoid questions, with the goal of finding quick and reliable ways of matching a query to an answer.
no code implementations • COLING 2022 • Aikaterini-Lida Kalouli, Rita Sevastjanova, Christin Beck, Maribel Romero
With the success of contextualized language models, much research explores what these models really learn and in which cases they still fail.
no code implementations • ACL 2021 • Rita Sevastjanova, Aikaterini-Lida Kalouli, Christin Beck, Hanna Sch{\"a}fer, Mennatallah El-Assady
Despite the success of contextualized language models on various NLP tasks, it is still unclear what these models really learn.
no code implementations • COLING 2020 • Aikaterini-Lida Kalouli, Rita Sevastjanova, Valeria de Paiva, Richard Crouch, Mennatallah El-Assady
Advances in Natural Language Inference (NLI) have helped us understand what state-of-the-art models really learn and what their generalization power is.
2 code implementations • COLING 2020 • Aikaterini-Lida Kalouli, Richard Crouch, Valeria de Paiva
Despite the advances in Natural Language Inference through the training of massive deep models, recent work has revealed the generalization difficulties of such models, which fail to perform on adversarial datasets with challenging linguistic phenomena.
no code implementations • WS 2019 • Aikaterini-Lida Kalouli, Valeria de Paiva, Richard Crouch
First, we propose that the semantic and not the syntactic contribution of each component of a noun phrase should be considered, so that the resulting composed vectors express more of the phrase meaning.
no code implementations • WS 2019 • Aikaterini-Lida Kalouli, Rebecca Kehlbeck, Rita Sevastjanova, Katharina Kaiser, Georg A. Kaiser, Miriam Butt
The study of language change through parallel corpora can be advantageous for the analysis of complex interactions between time, text domain and language.
1 code implementation • WS 2019 • Aikaterini-Lida Kalouli, Richard Crouch, Valeria de Paiva
This work focuses on an example of the third and less studied approach: it extends the Graphical Knowledge Representation (GKR) to include distributional features and proposes a division of semantic labour between the distributional and structural/symbolic features.
1 code implementation • WS 2019 • Aikaterini-Lida Kalouli, Annebeth Buis, Livy Real, Martha Palmer, Valeria de Paiva
The vast amount of research introducing new corpora and techniques for semi-automatically annotating corpora shows the important role that datasets play in today{'}s research, especially in the machine learning community.
no code implementations • WS 2018 • Aikaterini-Lida Kalouli, Richard Crouch
This paper describes the first version of an open-source semantic parser that creates graphical representations of sentences to be used for further semantic processing, e. g. for natural language inference, reasoning and semantic similarity.
no code implementations • SEMEVAL 2018 • Richard Crouch, Aikaterini-Lida Kalouli
A position paper arguing that purely graphical representations for natural language semantics lack a fundamental degree of expressiveness, and cannot deal with even basic Boolean operations like negation or disjunction.