Papers by Joachim Wagner
Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A Faroese Case Study (D19-61)
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| Challenge: | Cross-lingual dependency parsing involves transferring syntactic knowledge from one language to another. |
| Approach: | They compare two approaches to cross-lingual dependency parsing using monolingual source models and a polyglot model which is trained on the combination of all source languages. |
| Outcome: | The proposed methods improve low-resource dependency parsers by transferring syntactic knowledge from one language to another. |
Investigating the Saliency of Sentiment Expressions in Aspect-Based Sentiment Analysis (2023.findings-acl)
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| Challenge: | In a set of masking experiments, we examine the extent to which the tokens identified as salient by LIME and a gradient-based method are being used by the classifier. |
| Approach: | They use a BERT BASE model to mask the sentiments of an English dataset and find that both methods produce faithful rationales. |
| Outcome: | The proposed classifier outperforms both the gradient-based and black-box saliency methods on the SemEval 2016 english dataset. |
Beyond Binary: Towards Embracing Complexities in Cyberbullying Detection and Intervention - a Position Paper (2024.lrec-main)
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Kanishk Verma, Kolawole John Adebayo, Joachim Wagner, Megan Reynolds, Rebecca Umbach, Tijana Milosevic, Brian Davis
| Challenge: | Existing methods for CB detection oversimplify the problem of CB as a binary classification task. |
| Approach: | They propose to use large language models to generate CB-related datasets . they propose to combine cognitive and linguistic models to help identify CB incidents . |
| Outcome: | The proposed approach aims to help researchers and policymakers make informed decisions . it uses large language models such as Claude-2 and Llama2-Chat to generate CB-related datasets . |
gaBERT — an Irish Language Model (2022.lrec-1)
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James Barry, Joachim Wagner, Lauren Cassidy, Alan Cowap, Teresa Lynn, Abigail Walsh, Mícheál J. Ó Meachair, Jennifer Foster
| Challenge: | We compare gaBERT to multilingual BERT and the monolingual Irish WikiBERT and show that gaBERt provides better representations for downstream parsing tasks. |
| Approach: | They propose a monolingual BERT model for the Irish language that provides better representations for a downstream parsing task. |
| Outcome: | The proposed model performs better than the multilingual BERT and the monolingual Irish WikiBERT on a parsing task. |
BullyBench: Youth & Experts-in-the-loop Framework for Intrinsic and Extrinsic Cyberbullying NLP Benchmarking (2025.emnlp-industry)
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Kanishk Verma, Sri Balaaji, Joachim Wagner, Arefeh Kazemi, Darragh Mccashin, Isobel Walsh@dcu, Sayani Basak, Sinan Asci, Yelena Cherkasova, Alexandros Poulis, James Ohiggins Norman, Rebecca Umbach Umbach, Tijana Milosevic, Brian Davis
| Challenge: | Existing youth-focused CB datasets lack conversational realism and ethical youth involvement with little or no evaluation of their social plausibility. |
| Approach: | They propose a youth-in-the-loop dataset “BullyBench” that incorporates a structured intrinsic quality evaluation with experts-in the-looop (social scientists, psychologists, and content moderators) they perform extrinsic baseline evaluation by benchmarking encoder- and decoder-only language models for multi-class CB role classification. |
| Outcome: | The proposed dataset is evaluated by a team of social scientists, psychologists, and content moderators to assess its quality, relevance, and coherence. |
Revisiting Tri-training of Dependency Parsers (2021.emnlp-main)
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| Challenge: | Pre-trained word embeddings and self-training have been used in dependency parsing tasks for years. |
| Approach: | They compare tri-training and pretrained word embeddings in dependency parsing . they use language-specific FastText and ELMo embedds and multilingual BERT embedders . |
| Outcome: | The proposed methods are tri-training and pretrained word embeddings. |
Treebank Embedding Vectors for Out-of-Domain Dependency Parsing (2020.acl-main)
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| Challenge: | a recent advance in monolingual dependency parsing is the idea of a treebank embedding vector . this allows the model to prefer training data from one treebank over another at test time . |
| Approach: | They propose a method to predict a treebank vector for sentences that do not come from a particular treebank . they also explore what happens when they move away from predefined treebank embedding vectors . |
| Outcome: | The proposed method can predict treebank vectors for sentences that do not come from a treebank used in training with sufficient accuracy for nine out of ten languages. |