1 code implementation • LREC 2022 • Tatiana Bladier, Kilian Evang, Valeria Generalova, Zahra Ghane, Laura Kallmeyer, Robin Möllemann, Natalia Moors, Rainer Osswald, Simon Petitjean
This paper describes the first release of RRGparbank, a multilingual parallel treebank for Role and Reference Grammar (RRG) containing annotations of George Orwell’s novel 1984 and its translations.
no code implementations • COLING 2022 • Kilian Evang, Laura Kallmeyer, Jakub Waszczuk, Kilu von Prince, Tatiana Bladier, Simon Petitjean
Starting from an existing RRG parser, we propose two strategies for low-resource parsing: first, we extend the parsing model into a cross-lingual parser, exploiting the parallel data in the high-resource language and unsupervised word alignments by providing internal states of the source-language parser to the target-language parser.
1 code implementation • IWCS (ACL) 2021 • Esther Seyffarth, Younes Samih, Laura Kallmeyer, Hassan Sajjad
This paper addresses the question to which extent neural contextual language models such as BERT implicitly represent complex semantic properties.
1 code implementation • ACL 2022 • Regina Stodden, Laura Kallmeyer
TS-ANNO can be used for i) sentence–wise alignment, ii) rating alignment pairs (e. g., w. r. t.
1 code implementation • LREC (MWE) 2022 • Rafael Ehren, Laura Kallmeyer, Timm Lichte
In this paper we examine a BiLSTM architecture for disambiguating verbal potentially idiomatic expressions (PIEs) as to whether they are used in a literal or an idiomatic reading with respect to explainability of its decisions.
no code implementations • 2 Apr 2024 • Stephan Linzbach, Dimitar Dimitrov, Laura Kallmeyer, Kilian Evang, Hajira Jabeen, Stefan Dietze
Typically, designing these prompts is a tedious task because small differences in syntax or semantics can have a substantial impact on knowledge retrieval performance.
1 code implementation • 13 Nov 2023 • David Arps, Laura Kallmeyer, Younes Samih, Hassan Sajjad
We replicate the findings of M\"uller-Eberstein et al. (2022) on nonce test data and show that the performance declines on both MLMs and ALMs wrt.
1 code implementation • 31 Oct 2023 • Omar Momen, David Arps, Laura Kallmeyer
In this paper, we describe our submission to the BabyLM Challenge 2023 shared task on data-efficient language model (LM) pretraining (Warstadt et al., 2023).
1 code implementation • 30 May 2023 • Regina Stodden, Omar Momen, Laura Kallmeyer
13k sentence pairs) and a web-domain corpus (approx.
Ranked #1 on Text Simplification on DEplain-APA-doc
1 code implementation • 13 Apr 2022 • David Arps, Younes Samih, Laura Kallmeyer, Hassan Sajjad
We find that 4 pretrained transfomer LMs obtain high performance on our probing tasks even on manipulated data, suggesting that semantic and syntactic knowledge in their representations can be separated and that constituency information is in fact learned by the LM.
1 code implementation • COLING 2020 • Tatiana Bladier, Jakub Waszczuk, Laura Kallmeyer
We describe an approach to statistical parsing with Tree-Wrapping Grammars (TWG).
no code implementations • COLING 2020 • Esther Seyffarth, Laura Kallmeyer
We use ENCOW and VerbNet data to train classifiers to predict the instrument subject alternation and the causative-inchoative alternation, relying on count-based and vector-based features as well as perplexity-based language model features, which are intended to reflect each alternation{'}s felicity by simulating it.
no code implementations • WS 2020 • Rafael Ehren, Timm Lichte, Laura Kallmeyer, Jakub Waszczuk
Supervised disambiguation of verbal idioms (VID) poses special demands on the quality and quantity of the annotated data used for learning and evaluation.
no code implementations • LREC 2020 • Regina Stodden, Behrang Qasemizadeh, Laura Kallmeyer
In this system demonstration paper, we present an open-source web-based application with a responsive design for modular semantic frame annotation (SFA).
no code implementations • LREC 2020 • Regina Stodden, Laura Kallmeyer
In text simplification and readability research, several features have been proposed to estimate or simplify a complex text, e. g., readability scores, sentence length, or proportion of POS tags.
1 code implementation • WS 2019 • Jakub Waszczuk, Rafael Ehren, Regina Stodden, Laura Kallmeyer
We propose to tackle the problem of verbal multiword expression (VMWE) identification using a neural graph parsing-based approach.
no code implementations • SEMEVAL 2019 • Behrang QasemiZadeh, Miriam R. L. Petruck, Regina Stodden, Laura Kallmeyer, C, Marie ito
This paper presents Unsupervised Lexical Frame Induction, Task 2 of the International Workshop on Semantic Evaluation in 2019.
no code implementations • WS 2019 • Jens Fleischhauer, Thomas Gamerschlag, Laura Kallmeyer, Simon Petitjean
Complex predicates formed of a semantically {`}light{'} verbal head and a noun or verb which contributes the major part of the meaning are frequently referred to as {`}light verb constructions{'} (LVCs).
no code implementations • 23 Oct 2018 • Agata Savary, Simon Petitjean, Timm Lichte, Laura Kallmeyer, Jakub Waszczuk
Multiword expressions (MWEs) exhibit both regular and idiosyncratic properties.
no code implementations • COLING 2018 • Regina Stodden, Behrang Qasemizadeh, Laura Kallmeyer
We describe the TRAPACC system and its variant TRAPACCS that participated in the closed track of the PARSEME Shared Task 2018 on labeling verbal multiword expressions (VMWEs).
no code implementations • ACL 2018 • Tatiana Bladier, Andreas van Cranenburgh, Younes Samih, Laura Kallmeyer
We present ongoing work on data-driven parsing of German and French with Lexicalized Tree Adjoining Grammars.
no code implementations • SEMEVAL 2018 • Laura Kallmeyer, Behrang Qasemizadeh, Jackie Chi Kit Cheung
We present a method for unsupervised lexical frame acquisition at the syntax{--}semantics interface.
no code implementations • SEMEVAL 2017 • Behrang QasemiZadeh, Laura Kallmeyer
This paper describes the HHU system that participated in Task 2 of SemEval 2017, Multilingual and Cross-lingual Semantic Word Similarity.
no code implementations • CONLL 2017 • Younes Samih, Mohamed Eldesouki, Mohammed Attia, Kareem Darwish, Ahmed Abdelali, Hamdy Mubarak, Laura Kallmeyer
Arabic dialects do not just share a common koin{\'e}, but there are shared pan-dialectal linguistic phenomena that allow computational models for dialects to learn from each other.
no code implementations • JEPTALNRECITAL 2017 • Behrang Qasemizadeh, Laura Kallmeyer, Aurelie Herbelot
Non-Negative Randomized Word Embedding We propose a word embedding method which is based on a novel random projection technique.
1 code implementation • 11 May 2017 • Behrang QasemiZadeh, Laura Kallmeyer
We propose a new fast word embedding technique using hash functions.
no code implementations • WS 2017 • Younes Samih, Mohammed Attia, Mohamed Eldesouki, Ahmed Abdelali, Hamdy Mubarak, Laura Kallmeyer, Kareem Darwish
The automated processing of Arabic Dialects is challenging due to the lack of spelling standards and to the scarcity of annotated data and resources in general.
no code implementations • WS 2016 • Mohammed Attia, Suraj Maharjan, Younes Samih, Laura Kallmeyer, Thamar Solorio
The evaluation results of our system on the test set is 88. 1{\%} (79. 0{\%} for TRUE only) f-measure for Task-1 on detecting semantic similarity, and 76. 0{\%} (42. 3{\%} when excluding RANDOM) for Task-2 on identifying finer-grained semantic relations.