Papers by Michael Strube
A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification (2020.findings-emnlp)
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| Challenge: | Existing models for fine-grained entity typing have a hierarchical structure . prior work has integrated only explicit hierarchic information by formulating a hierarchy-aware loss or by representing instances and labels in a joint Euclidean embedding space. |
| Approach: | They propose a fully hyperbolic model for multi-class multi-label classification that performs all operations in hyperbolical space. |
| Outcome: | The proposed model performs all operations in hyperbolic space on two challenging datasets and shows it is comparable to state-of-the-art methods on fine-grained classification with remarkable reduction of parameter size. |
BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages (L18-1)
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| Challenge: | In an evaluation using fine-grained entity typing as testbed, BPEmb performs competitively . pre-trained subword embeddings for BPE units are commonly available . |
| Approach: | They present a collection of pre-trained subword embeddings in 275 languages . they use fine-grained entity typing as testbed to evaluate BPEmb . |
| Outcome: | The proposed method performs better than other methods, but requires less resources and no tokenization. |
Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation (2023.acl-long)
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| Challenge: | Discourse connectives are words or phrases that signal the presence of a discourse relation. |
| Approach: | They propose a model that generates discourse connectives between arguments and predicts discourse relations based on the generated connectives. |
| Outcome: | The proposed model outperforms baselines on three datasets and is highly accurate. |
Centering-based Neural Coherence Modeling with Hierarchical Discourse Segments (2020.emnlp-main)
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| Challenge: | Prior studies of coherence focused on identifying semantic relations between adjacent sentences. |
| Approach: | They propose a coherence model which takes discourse structural information into account without relying on human annotations. |
| Outcome: | The proposed model performs state-of-the-art on automated essay scoring and assessing writing quality tasks. |
DiscoScore: Evaluating Text Generation with BERT and Discourse Coherence (2023.eacl-main)
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| Challenge: | DiscoScore is a parametrized discourse metric that uses BERT to model discourse coherence . it is weak when operated at system level, and is therefore not reliable in a way to spot improvements . |
| Approach: | They propose a parametrized discourse metric which uses BERT to model discourse coherence from different perspectives. |
| Outcome: | The proposed model outperforms existing models on document-level machine translation and summarization. |
What Causes the Failure of Explicit to Implicit Discourse Relation Recognition? (2024.naacl-long)
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| Challenge: | Prior work claimed that explicit classifiers perform poorly in implicit scenarios . a label shift occurs after connectives are removed, but no empirical evidence supports this claim . |
| Approach: | They propose to prove that the discourse relations expressed by some explicit instances will change when connectives disappear. |
| Outcome: | The proposed methods outperform strong baselines on PDTB 2.0, PDTT 3.0, and the GUM dataset. |
A Neural Local Coherence Model for Text Quality Assessment (D18-1)
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| Challenge: | Existing approaches to local coherence modeling capture text relatedness at the level of sentence-to-sentence transitions. |
| Approach: | They propose a local coherence model that captures the flow of what connects adjacent sentences . they represent the semantics of a sentence by a vector and capture its state at each word . |
| Outcome: | The proposed model is beneficial for readability assessment and essay scoring tasks. |
Incorporating Centering Theory into Neural Coreference Resolution (2022.naacl-main)
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| Challenge: | Recent years, transformer-based coreference resolution systems have achieved remarkable improvements on the CoNLL dataset. |
| Approach: | They propose to incorporate centering transitions derived from centering theory into a neural coreference model by using a graph. |
| Outcome: | The proposed model improves on pronoun resolution in long documents, formal well-structured text, and clusters with scattered mentions. |
Adapting Deep Learning Methods for Mental Health Prediction on Social Media (D19-55)
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| Challenge: | a quarter of the population in Europe suffers from an episode of a mental disorder in their life, according to the World Health Organization . text analysis of rich resources like social media can contribute to deeper understanding of mental health and provide means for their early detection. |
| Approach: | They propose to use a hierarchical attention network to predict if a user suffers from one of nine disorders to adapt a deep neural model to the task. |
| Outcome: | The proposed model outperforms previous benchmarks for four out of nine disorders in a binary classification task on social media. |
A Large Harvested Corpus of Location Metonymy (2020.lrec-1)
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| Challenge: | Existing datasets of metonymy are either too small in size or lack sufficient coverage. |
| Approach: | They propose a new corpus of location metonymy called WiMCor that is large in size and has high coverage. |
| Outcome: | The proposed corpus is large in size and has high coverage. |
Cross-lingual Science Journalism: Select, Simplify and Rewrite Summaries for Non-expert Readers (2023.acl-long)
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| Challenge: | CSJ is a task of text simplification and cross-lingual scientific summarization to facilitate science journalists’ work. |
| Approach: | They propose to combine CSJ tasks SELECT, SIMPLIFY and REWRITE to produce cross-lingual simplified science summaries for non-expert readers. |
| Outcome: | The proposed task outperforms existing solutions on Wikipedia and can serve as a strong baseline for future work. |
Incremental Neural Lexical Coherence Modeling (2020.coling-main)
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| Challenge: | Recent work on pretrained language models has led to significant improvements in a range of NLP tasks. |
| Approach: | They propose a coherence model which interprets sentences incrementally to capture lexical relations between them. |
| Outcome: | The proposed model interprets sentences incrementally to capture lexical relations between them. |
Consistent Discourse-level Temporal Relation Extraction Using Large Language Models (2025.findings-emnlp)
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| Challenge: | Recent advances in large language models (LLMs) have spurred research on temporal relation extraction tasks. |
| Approach: | They propose a framework to improve LLMs’ temporal relation extraction capabilities using context selection, prompts inspired by Allen’s interval algebra and reflection-based consistency learning. |
| Outcome: | The proposed framework improves LLMs’ extraction capabilities by focusing on context selection, prompts inspired by Allen’s interval algebra and reflection-based consistency learning. |
Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection (P19-1)
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| Challenge: | Existing methods to evaluate maximum spans tangle coreference evaluation with mention boundary detection . however, this method is costly and does not scale to large corpora. |
| Approach: | They propose an algorithm for automatically extracting minimum spans to benefit from minimum span evaluation in all corpora. |
| Outcome: | The proposed algorithm is consistent with those manually annotated by experts. |
Graph-based Clustering for Detecting Semantic Change Across Time and Languages (2024.eacl-long)
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| Challenge: | Existing approaches to detect semantic change using contextualized embeddings are underperforming . a graph-based clustering approach captures nuanced changes in word senses across time and languages . |
| Approach: | They propose a graph-based clustering approach to capture nuanced changes in word senses across time and languages. |
| Outcome: | The proposed approach surpasses previous approaches in the SemEval2020 binary classification task across four languages. |
Discourse Relation-Enhanced Neural Coherence Modeling (2025.acl-long)
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| Challenge: | Existing work on coherence modeling has focused on integrating entity-based models. |
| Approach: | They propose a model that integrates text- and relation-based features for coherence assessment using position-aware attention and a visible matrix. |
| Outcome: | The proposed model improves baselines on two benchmarks and shows that relation features are important for coherence modeling. |
Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers (D18-1)
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| Challenge: | Recent coreference resolvers have notable improvements on the CoNLL evaluation sets, but struggle to generalize properly to new datasets. |
| Approach: | They investigate the role of linguistic features in building more generalizable coreference resolvers . they show that employing features and subsets of their values that are informative for coreference resolution improves generalization . |
| Outcome: | The proposed system achieves state-of-the-art results on WikiCoref, compared with a system trained on CoNLL. |
Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks (2023.acl-long)
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| Challenge: | Existing methods focus on a single document’s coherence patterns, ignoring the underlying correlation between documents. |
| Approach: | They propose a GCN-based coherence model that captures structural similarities between documents by mining subgraph patterns and a heterogeneous graph for the training corpus. |
| Outcome: | The proposed model outperforms baseline models on discourse coherence and automated essay scoring tasks. |
Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation (P19-1)
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| Challenge: | Pretrained contextual and non-contextual subword embeddings are available in over 250 languages, allowing massively multilingual NLP. |
| Approach: | They compare pretrained contextual and non-contextual subword embeddings with a contextual representation method, namely BERT, on multilingual named entity recognition and part-of-speech tagging. |
| Outcome: | The proposed method outperforms non-contextual embeddings on multilingual named entity recognition and part-of-speech tagging. |
Investigating Multilingual Coreference Resolution by Universal Annotations (2023.findings-emnlp)
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| Challenge: | Existing systems for multilingual coreference resolution have been challenging due to linguistic diversity and complexity of different languages. |
| Approach: | They propose a multilingual coreference dataset with universal morphosyntactic and coreference annotations. |
| Outcome: | The proposed dataset improves the baseline system by 0.9% . the proposed dataset is based on the framework of Universal Dependencies 2 . |
Linguistic Cues for LLM-based Implicit Discourse Relation Classification (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) have been successful in many NLP tasks, but they struggle to capture subtle lexical relations between arguments. |
| Approach: | They propose a strategy that enriches arguments with explicit lexical-level semantic cues before fine-tuning. |
| Outcome: | The proposed approach improves F1 scores in cross-domain scenarios by more than 10 points compared to baselines. |
Entity-based Neural Local Coherence Modeling (2022.acl-long)
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| Challenge: | Recent neural coherence models encode the input document using large-scale pretrained language models. |
| Approach: | They propose an entity-based neural local coherence model which is linguistically more sound than previous models. |
| Outcome: | The proposed model outperforms existing models on three downstream tasks. |
Joint Modeling of Entities and Discourse Relations for Coherence Assessment (2025.emnlp-main)
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| Challenge: | Existing work on coherence modeling focuses on entity features or discourse relation features, with little attention given to combining the two. |
| Approach: | They propose two methods for jointly modeling entities and discourse relations for coherence assessment. |
| Outcome: | The proposed methods significantly improve the performance of coherence models on three benchmark datasets. |
Transparent, Efficient, and Robust Word Embedding Access with WOMBAT (C18-2)
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| Challenge: | WOMBAT is a Python tool that supports accessing word embeddings from code. |
| Approach: | WOMBAT is a Python tool which supports NLP practitioners in accessing word embeddings from code. |
| Outcome: | WOMBAT is a Python tool which supports accessing word embeddings from code. |