Papers with German
Cross-lingual Semantic Representation for NLP with UCCA (2020.coling-tutorials)
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| Challenge: | introductory tutorial to UCCA, a symbolic meaning representation for semantic representations. |
| Approach: | This tutorial introduces UCCA, a cross-linguistically applicable framework for semantic representation . it will provide a detailed introduction to the UCca annotation guidelines, design philosophy and available resources . |
| Outcome: | The tutorial will provide a detailed introduction to the UCCA framework and compare it to other meaning representations. |
Reducing Confusion in Active Learning for Part-Of-Speech Tagging (2021.tacl-1)
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| Challenge: | Existing algorithms for annotating parts of speech are not optimal for all languages. |
| Approach: | They propose to use a data selection algorithm to select useful training samples to minimize annotation cost. |
| Outcome: | The proposed strategy outperforms existing strategies on six typologically diverse languages. |
Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task (D19-64)
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| Challenge: | Existing methods to learn multimodal multilingual embeddings for text and image retrieval tasks are limited to English. |
| Approach: | They propose a new approach to learn multimodal multilingual embeddings for matching images and captions in two languages by combing two existing objective functions and adapting alignment between existing languages. |
| Outcome: | The proposed model achieves state-of-the-art in retrieval and caption-caption tasks while adapting existing language alignments. |
Identifying and Resolving Annotation Changes for Natural Language Understanding (2021.naacl-industry)
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| Challenge: | Annotation conflict resolution is crucial for machine learning, says a new study . past work on annotation conflict resolution assumed data is collected at once . a a supervised neural model can resolve conflicts in data annotation but requires access to high-quality data . |
| Approach: | They propose an approach to resolve annotation conflicts in a real-world context using a German dialog system. |
| Outcome: | The proposed approach improves on a real-world dataset with 3.5M utterances in German. |
Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing (2024.findings-emnlp)
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| Challenge: | Cross-lingual AMR parsing is a task of predicting AMR graphs in a target language when training data is available only in . et al. (2018) evaluated meta-learning for cross-lingual parse in Croatian, Farsi, Korean, Chinese, and French. |
| Approach: | They propose to use meta-learning to tackle cross-lingual AMR parsing in a target language . they evaluate their models in k-shot scenarios and compare them to classical joint learning . |
| Outcome: | The proposed model performs better in 0-shot evaluation for Croatian, Farsi, Korean, Chinese, and French. |
CRAB: A Benchmark for Evaluating Curation of Retrieval-Augmented LLMs in Biomedicine (2025.emnlp-industry)
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| Challenge: | Recent development in Retrieval-Augmented Large Language Models (LLMs) have shown great promise in biomedical applications. |
| Approach: | They propose a multilingual benchmark to evaluate retrieval-augmented large language models' curation ability. |
| Outcome: | The proposed benchmark is available in English, French, German and Chinese. |
Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task (D19-66)
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| Challenge: | Existing methods to learn multimodal multilingual embeddings for text and image retrieval tasks are limited to English. |
| Approach: | They propose a new approach to learn multimodal multilingual embeddings for matching images and captions in two languages by combing two existing objective functions and adapting alignment between existing languages. |
| Outcome: | The proposed model achieves state-of-the-art in retrieval and caption-caption tasks while adapting existing language alignments. |
Translating Headers of Tabular Data: A Pilot Study of Schema Translation (2021.emnlp-main)
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| Challenge: | Schema translation is not well studied in the community because of morphological difference and context difference between plain text and tabular data. |
| Approach: | They propose a schema translation model augmented with schema context . they model a target header and its context as a directed graph to represent their entities . |
| Outcome: | The proposed model outperforms state-of-the-art models on schema translation . it uses a graph to represent entity types and relations, and a relational-aware transformer . |
BLEU might be Guilty but References are not Innocent (2020.emnlp-main)
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| Challenge: | Using a method to collect references and compare their value with human evaluations, we show that multi-reference BLEU does not improve the correlation for high quality output. |
| Approach: | They propose a method to compare the quality of automated metrics by analyzing references and comparing them with human evaluations. |
| Outcome: | The proposed method improves correlation with all modern evaluation metrics including embedding-based methods. |
Improving Character-Based Decoding Using Target-Side Morphological Information for Neural Machine Translation (N18-1)
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| Challenge: | Morphologically complex words (MCWs) are multi-layer structures consisting of different subunits, each of which carries semantic information and has a specific syntactic role. |
| Approach: | They propose an extension to the state-of-the-art model which works at the character level and boosts the decoder with target-side morphological information. |
| Outcome: | The proposed model improves on the state-of-the-art model and can be extended to include morphologically complex words (MCWs) in three languages. |
Syntax-Based Attention Masking for Neural Machine Translation (2021.naacl-srw)
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| Challenge: | Existing approaches to extend transformers to source-side trees are linearized into sequences, but they are limited by positional encodings. |
| Approach: | They propose a method for extending transformers to source-side trees by using masks based on tree positions . they define a number of masks that limit self-attention based upon relationships among tree nodes . |
| Outcome: | The proposed method improves on translations from English to germany and English to english and germany by +2.1 BLEU. |
Continuous Model Improvement for Language Understanding with Machine Translation (2021.naacl-industry)
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Abdalghani Abujabal, Claudio Delli Bovi, Sungho Ryu, Turan Gojayev, Fabian Triefenbach, Yannick Versley
| Challenge: | a simple translation-test approach would fail the latency requirements of a live environment. |
| Approach: | They show that annotating unlabeled utterances offline can improve performance . they demonstrate that an extrinsic evaluation can improve the performance if manual data is available . |
| Outcome: | The proposed method improves performance in an extrinsic evaluation setting with real-world commercial dialog system in german. |
Natural Language Processing for Multilingual Task-Oriented Dialogue (2022.acl-tutorials)
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| Challenge: | a tutorial will examine the challenges and gaps in multilingual ToD research . multilingual systems are difficult to build, and are limited to English and other languages . |
| Approach: | This tutorial will discuss the importance of multilingual task-oriented dialogue systems . it will provide an overview of current research gaps, challenges and initiatives related to multilingual ToD systems - with a particular focus on their connections to current research and challenges in multilingual and low-resource NLP. |
| Outcome: | This tutorial will provide an overview of current research gaps, challenges and initiatives related to multilingual ToD systems. |
German and French Neural Supertagging Experiments for LTAG Parsing (P18-3)
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| Challenge: | Lexicalized Tree Adjoining Grammars are a linguistically motivated grammar formalism that allows parsers to express linguistic generalizations that are not captured by statistical parsing. |
| Approach: | They propose a supertagging approach combined with deep learning to extract LTAG supertags from the French Treebank and propose n-best supertailing for German and French. |
| Outcome: | The proposed supertagging approach is able to extract LTAG supertags from the French Treebank and n-best supertracking for German and German. |
Multilingual Language Models Predict Human Reading Behavior (2021.naacl-main)
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| Challenge: | Recent studies show that cognitively motivated "attention" mechanism in neural models is not a good indicator for relative importance. |
| Approach: | They compare the performance of language-specific and multilingual pretrained transformer models to predict reading time measures reflecting natural human sentence processing. |
| Outcome: | The proposed models predict reading time measures on Dutch, English, German, and Russian texts. |
Automated Cross-language Intelligibility Analysis of Parkinson’s Disease Patients Using Speech Recognition Technologies (P19-2)
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| Challenge: | PD is the second most common neurodegenerative disorder after Alzheimers disease . speech impairments are one of the earliest manifestations in PD patients . |
| Approach: | They propose to analyze the speech signals of PD patients and healthy control subjects in three different languages: German, Spanish, and Czech. |
| Outcome: | The proposed model can discriminate between PD patients and HC subjects even when the language used for train and test is different. |
An Attentive Recurrent Model for Incremental Prediction of Sentence-final Verbs (2020.findings-emnlp)
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| Challenge: | Using an attention-based neural model, we can incrementally predict verbs on incomplete sentences in Japanese and German SOV sentences. |
| Approach: | They propose a synonym-aware neural model to incrementally predict final verbs on incomplete sentences in Japanese and German SOV sentences. |
| Outcome: | The proposed model outperforms existing models in predicting most frequent verbs in Japanese and German . larger datasets always help with predicting the sentencefinal verbs, suggesting larger dataset could be used to reduce translation latency. |
Familiar words but strange voices: Modelling the influence of speech variability on word recognition (2021.eacl-srw)
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| Challenge: | Despite the lack of acoustic-phonetic invariance in speech, listeners can reliably recognize spoken words despite the lack aural-phonemic invariancy. |
| Approach: | They propose a deep neural model which is trained to retrieve the meaning of a word given its spoken form, a task which resembles that faced by a human listener. |
| Outcome: | The proposed model is more sensitive to dialectical variation than gender variation and more related to related languages. |
A corpus of metaphors as register markers (2023.findings-eacl)
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| Challenge: | Using corpus annotation, we show huge differences in metaphor usage between different registers and specific properties of registers. |
| Approach: | They present their work on corpus annotation for metaphor in germany . they focus on metaphors that can serve as register markers and be reliably indentified . |
| Outcome: | The proposed corpus annotations show huge differences in metaphor usage between different registers and specific properties of registers. |
More than just Frequency? Demasking Unsupervised Hypernymy Prediction Methods (2021.findings-acl)
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| Challenge: | Using unsupervised methods of hypernymy prediction, we show that the predictions of three methods overlap and are highly correlated with frequency-based predictions. |
| Approach: | They compare unsupervised methods of hypernymy prediction to supervised methods . they show that the methods overlap and are highly correlated with frequency-based predictions . |
| Outcome: | The proposed methods overlap and are highly correlated with frequency-based predictions across English and German datasets. |
Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance (2025.naacl-industry)
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Somnath Banerjee, Avik Halder, Rajarshi Mandal, Sayan Layek, Ian Soboroff, Rima Hazra, Animesh Mukherjee
| Challenge: | Pretrained language models (PLMs) have revolutionized NLP but amplify linguistic inequities in multilingual applications. |
| Approach: | They evaluate pretrained language models including Mistral, TowerInstruct, OpenHathi, Tamil-Llama, and Kan-Lama across eight languages spanning high-resource and low-resourced settings. |
| Outcome: | The proposed models fail to bridge linguistic divides and are inefficient when compared to other models. |
VOLIMET: A Parallel Corpus of Literal and Metaphorical Verb-Object Pairs for English–German and English–French (2024.starsem-1)
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| Challenge: | Metaphorical language is a complex interplay of cultural and linguistic elements that characterizes metaphorical language . a corpus of parallel sentences containing gold standard alignments of metaphorical verb-object pairs and literal paraphrases is presented . |
| Approach: | They propose to analyze metaphorical verb-object pairs and literal paraphrases in parallel sentences from English to German and French. |
| Outcome: | The proposed corpus of 2,916 parallel sentences reveals monolingual patterns for metaphorical vs. literal uses in English . cross-lingually, the results show a rich variability in translations as well as different behaviors for the two target languages . |
Counterfactual Data Augmentation for Neural Machine Translation (2021.naacl-main)
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| Challenge: | Neural machine translation models often rely on large-scale parallel corpora for training, exhibiting degraded performance on low-resource languages. |
| Approach: | They propose a method that interprets language models and phrasal alignment causally and generates augmented parallel translation corpora by sampling new source phrases from a masked language model. |
| Outcome: | The proposed method improves translation, backtranslation and translation robustness on IWSLT’15 English Vietnamese, WMT’17 English - German, and WMT'18 English – Turkish. |
ANHALTEN: Cross-Lingual Transfer for German Token-Level Reference-Free Hallucination Detection (2024.acl-srw)
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| Challenge: | ANHALTEN is a new evaluation dataset that extends the English hallucination detection dataset to German. |
| Approach: | They propose a dataset that extends the English hallucination detection dataset to German . they show that larger context length leads to better halluciation detection in german . |
| Outcome: | ANHALTEN is the first evaluation dataset that extends the English hallucination detection dataset to German. |
DRS Parsing as Sequence Labeling (2022.starsem-1)
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| Challenge: | a new semantic parser for English, German, Italian, and Dutch discourse representation structures is developed . we present a system that maps tokens to finite set of meaning fragments and is more transparent . a comprehensive error analysis highlights areas for future work on semantic parses . |
| Approach: | They propose a fully trainable semantic parser for English, German, Italian, and Dutch discourse representation structures that maps each token to one of a finite set of meaning fragments. |
| Outcome: | The proposed system is more transparent and useful for human-in-the-loop annotations. |
Trainable, Multiword-aware Linguistic Tokenization Using Modern Neural Networks (2026.eacl-srw)
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| Challenge: | Tokenization is a fundamental task in natural language processing that forms the first step of many pipelines. |
| Approach: | They propose to use a standard tokenizer trained without MWE-awareness as a baseline and a character-level SRN+CRF model to train token-level models. |
| Outcome: | The proposed tokenizers are based on a character-level and token-level sequence labeling problem and are consistent with the proposed pipelines. |
Towards Personalised and Document-level Machine Translation of Dialogue (2021.eacl-srw)
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| Challenge: | State-of-the-art (SOTA) neural machine translation systems translate texts at sentence level, ignoring context. |
| Approach: | They propose to integrate extra-textual information into the translation process for the domain of dialogue extracted from TV subtitles in five languages: English, Brazilian Portuguese, German, French and Polish. |
| Outcome: | The proposed systems translate texts at sentence level, ignoring context . there are no readily available robust evaluation metrics for them . |
Zero-shot Cross-lingual Conversational Semantic Role Labeling (2022.findings-naacl)
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| Challenge: | Xu et al., 2021: conversational semantic role labeling is under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training. |
| Approach: | They propose a model that implicitly learns conversational structure-aware representations with hierarchical encoders and elaborately designed pre-training objectives. |
| Outcome: | The proposed model outperforms baselines on English CSRL tests by large margins . it will facilitate the research of non-Chinese dialogue tasks which suffer from ellipsis and anaphora . |
Multi-lingual neural title generation for e-Commerce browse pages (N18-3)
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| Challenge: | e-Commerce websites are automatically generating millions of browse pages . manual creation of titles is infeasible due to the huge number of browse page types . |
| Approach: | They propose to use sequence-to-sequence models to generate titles for languages . they train the models on multi-lingual data, thereby creating one joint model . |
| Outcome: | The proposed model can generate titles in three different languages, with a focus on low-resource French. |
KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning (C18-2)
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Florian Dessloch, Thanh-Le Ha, Markus Müller, Jan Niehues, Thai-Son Nguyen, Ngoc-Quan Pham, Elizabeth Salesky, Matthias Sperber, Sebastian Stüker, Thomas Zenkel, Alexander Waibel
| Challenge: | In today's globalized world, communication is difficult and often the language barrier still prevents communication. |
| Approach: | They have developed a low-latency translation system that is adapted to lectures and covers several language pairs. |
| Outcome: | The proposed system improves performance but also covers several European languages. |
ViClaim: A Multilingual Multilabel Dataset for Automatic Claim Detection in Videos (2025.emnlp-main)
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| Challenge: | Existing efforts in misinformation detection focus on written text, leaving a significant gap in addressing the complexity of spoken text in video transcripts. |
| Approach: | They propose to annotate video transcripts in three languages and six topics using a custom annotation tool. |
| Outcome: | The proposed tool shows strong cross-validation performance but challenges for generalization to unseen topics. |
Revisiting Low-Resource Neural Machine Translation: A Case Study (P19-1)
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| Challenge: | Recent research has shown that neural machine translation models are highly data-inefficient and underperform phrase-based statistical machine translation (PBSMT) in low-resource settings. |
| Approach: | They propose to use auxiliary data to train low-resource neural machine translation systems without auxiliary monolingual or multilingual data. |
| Outcome: | The proposed methods outperform PBSMT and other statistical machine translation models in Korean–English with minimal data. |
Does Character-level Information Always Improve DRS-based Semantic Parsing? (2023.starsem-1)
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| Challenge: | incorporating character-level information does not improve the performance in English and German, and is not sensitive to correct character order in Dutch. |
| Approach: | They propose to incorporate character-level representations into a neural semantic parser for Discourse Representation Structures and to test their performance using order of character sequences. |
| Outcome: | The proposed parser improves in English, German, Dutch, and Italian in four languages. |
Neural Unsupervised Parsing Beyond English (D19-61)
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| Challenge: | Unsupervised parsing is a task that can be learned without substantial prior knowledge. |
| Approach: | They train an unsupervised model for Arabic, Chinese, English, and German to learn syntactic structure from unlabeled text. |
| Outcome: | The PRPN architecture outperforms trivial baselines and acquires at least some parsing ability for all languages. |
What about “em”? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns (2023.acl-long)
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| Challenge: | Wrong pronoun translations can discriminate against marginalized groups, e.g., non-binary individuals. |
| Approach: | They compare 3rd-person pronoun translations to five other languages . they propose to address gender exclusivity in future research . |
| Outcome: | The proposed method compares translations of gendered vs. gender-neutral pronouns from english to five other languages and vice versa. |
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation (2020.coling-main)
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| Challenge: | Existing models for sentiment analysis over tweets require a substantial amount of text to adapt to a domain where the syntax is different. |
| Approach: | They propose to use a multilingual transformer model to train over tweets in five different languages to adapt the model to non-English languages. |
| Outcome: | The proposed model improves over small corpora of tweets in non-English languages. |
CompoundPiece: Evaluating and Improving Decompounding Performance of Language Models (2023.emnlp-main)
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| Challenge: | Currently, there is no dataset containing compound and non-compound words across languages . however, current LLMs perform poorly on words tokenized unfavorably by subword tokenization. |
| Approach: | They propose to use a Wiktionary dataset to evaluate large language models on decompounding . they find that current LLMs perform poorly on words tokenized unfavorably . |
| Outcome: | The proposed model outperforms the best unsupervised models by 13.9% accuracy on average. |
Bootstrapping Multilingual AMR with Contextual Word Alignments (2021.eacl-main)
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Janaki Sheth, Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Radu Florian, Salim Roukos, Todd Ward
| Challenge: | Abstract Meaning Representation (AMR) is a sentence-level graph that is biased towards English. |
| Approach: | They propose a technique for foreign-text-to-English AMR alignment using contextual word alignment between English and foreign language tokens. |
| Outcome: | The proposed technique outperforms the best results for German, Italian, Spanish and Chinese. |
SwissADT: An Audio Description Translation System for Swiss Languages (2025.naacl-industry)
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| Challenge: | despite advances in multilingual machine translation, lack of well-crafted AD data impedes development of audio description translation systems. |
| Approach: | They propose an audio description translation system for three main Swiss languages and English . they combine human expertise with the power of Large Language Models to improve quality . |
| Outcome: | The proposed system is designed to enhance accessibility for multilingual populations in Switzerland. |
The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification (2020.findings-emnlp)
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| Challenge: | Current approaches for relation classification are focused on the English language and require lots of training data with human annotations. |
| Approach: | They propose a baseline model based on Multilingual BERT and a new multilingual pretraining setup . they propose 'relationship classification' models that use distant supervision . |
| Outcome: | The proposed model significantly improves the baseline model with distant supervision. |
Building a Corpus from Handwritten Picture Postcards: Transcription, Annotation and Part-of-Speech Tagging (L18-1)
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| Challenge: | In this paper, we describe the processes and challenges of digitalisation, manual transcription, and manual annotation of over 11,000 postcards. |
| Approach: | They describe the processes and challenges of digitalisation, manual transcription, and manual annotation of over 11,000 postcards written in German and Swiss German. |
| Outcome: | The proposed system outperforms state-of-the-art taggers in the evaluation of the 'picture postcard corpus' containing over 11,000 handwritten postcards . |
Paraphrastic Representations at Scale (2022.emnlp-demos)
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| Challenge: | a new system allows users to train their own state-of-the-art paraphrastic sentence representations in a variety of languages. |
| Approach: | They propose a system that allows users to train their own paraphrastic sentence representations in a variety of languages. |
| Outcome: | The proposed models outperform previous models on monolingual and cross-lingual tasks and can be used on CPUs with little difference in inference speed. |
Constructing Multimodal Language Learner Texts Using LARA: Experiences with Nine Languages (2020.lrec-1)
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Elham Akhlaghi, Branislav Bédi, Fatih Bektaş, Harald Berthelsen, Matthias Butterweck, Cathy Chua, Catia Cucchiarin, Gülşen Eryiğit, Johanna Gerlach, Hanieh Habibi, Neasa Ní Chiaráin, Manny Rayner, Steinþór Steingrímsson, Helmer Strik
| Challenge: | LARA is an open source project that aims to support easy conversion of plain texts into online versions suitable for use by language learners. |
| Approach: | They propose to support easy conversion of plain texts into online versions suitable for use by language learners. |
| Outcome: | The proposed platform is suitable for creating texts in multiple languages via crowdsourcing techniques that can be used for teaching a language via reading and listening. |
Mitigating Language-Dependent Ethnic Bias in BERT (2021.emnlp-main)
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| Challenge: | Ethnic bias is one of the most prevalent social stereotypes. |
| Approach: | They propose to use a multilingual model and contextual word alignment to mitigate ethnic bias in monolingual BERT for English, German, Spanish, Korean, Turkish, and Chinese. |
| Outcome: | The proposed methods alleviate ethnic bias in English, German, Spanish, Korean, Turkish, and Chinese using a multilingual model and contextual word alignment of two monolingual models. |
Domain Specific Sub-network for Multi-Domain Neural Machine Translation (2022.aacl-short)
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| Challenge: | Neural machine translation (NMT) is based on transformer models that are trained on general data from a single language pair or multiple languages. |
| Approach: | They propose a method to make masks unique per domain to improve generalization to unseen domains. |
| Outcome: | The proposed method outperforms continue training on multi-domain data on German to English translation by 1.47 BLEU points and on new domains by 1.52 BLUE points. |
Grammatical Error Correction in Low-Resource Scenarios (D19-55)
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| Challenge: | Existing systems for grammatical error correction in English have been limited . however, there is limited progress on error correction of other languages . |
| Approach: | They propose a dataset on grammatical error correction for Czech and an annotated learner corpus for Russian and Czech. |
| Outcome: | The proposed model can reach new state-of-the-art on Czech, German and Russian datasets. |
Bootstrapping a Crosslingual Semantic Parser (2020.findings-emnlp)
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| Challenge: | Recent advances in semantic parsing are limited to English but professional translation can be prohibitively expensive. |
| Approach: | They adapt a semantic parser trained on a single language to new languages and multiple domains with minimal annotation. |
| Outcome: | The proposed approach achieves parsing accuracy within 2% of translation using only 50% of training data. |
Minimally-Augmented Grammatical Error Correction (D19-55)
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| Challenge: | Existing approaches to automatic grammatical error correction require error-labelled training data to achieve their best performance. |
| Approach: | They propose an unsupervised method that generates noise from inverted spell-checkers by using a synthetic error generation method. |
| Outcome: | The proposed method outperforms the current state-of-the-art for German and Russian GEC tasks without using real error-labelled training data. |
TLT-school: a Corpus of Non Native Children Speech (2020.lrec-1)
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| Challenge: | a corpus of speech utterances collected in schools of northern italy is being used to assess the performance of students learning both English and German. |
| Approach: | a corpus of speech utterances collected in schools of northern italy is described . the corpus is going to be freely distributed to scientific community . |
| Outcome: | The corpus of speech utterances collected in schools of northern italy is a "Trentino Language Testing" in schools" the data are used to assess the performance of students learning English and German . |
GEE! Grammar Error Explanation with Large Language Models (2024.findings-naacl)
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| Challenge: | Existing grammatical error correction tools do not provide natural language explanations of errors . a system needs to provide one-sentence explanations for each grammamatical errors in a pair of erroneous and corrected sentences. |
| Approach: | They propose a grammar error explanation task that uses one-sentence explanations for each grammatical error in a pair of erroneous and corrected sentences. |
| Outcome: | The proposed pipeline identifies grammar errors in German, Chinese, and English . human evaluation reveals that 93.9% of German errors, 96.4% of Chinese errors, and 92.20% of English errors are correctly detected and explained. |
Reducing cohort bias in natural language understanding systems with targeted self-training scheme (2023.acl-industry)
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| Challenge: | In deep learning models, it is hard to capture all the variations of the language that different users can use. |
| Approach: | They propose a framework that uses four active learning strategies to identify important samples coming from new users and a self training phase where a teacher model is trained from the first phase to expand the training data with relevant cohort utterances. |
| Outcome: | The proposed framework reduces the bias related to new customers in a digital voice assistant system by using two phases: a fixing phase and a self training phase. |
Discrete Cosine Transform as Universal Sentence Encoder (2021.acl-short)
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| Challenge: | Modern sentence encoders capture underlying linguistic characteristics of words . Discrete Cosine Transform (DCT) is an efficient alternative to averaging . |
| Approach: | They propose to use a Discrete Cosine Transform to generate universal sentence representations in different languages. |
| Outcome: | The proposed model captures the underlying syntactic characteristics of a given text without compromising practical efficiency. |
GenKnowSub: Improving Modularity and Reusability of LLMs through General Knowledge Subtraction (2025.acl-short)
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| Challenge: | Large language models (LLMs) struggle with zero-shot generalization due to entanglement of general knowledge and task-specific adaptations. |
| Approach: | They propose a modular framework that disentangles general knowledge and adaptations by constructing a library of task-specific LoRA modules alongside a general-domain LoRA. |
| Outcome: | The proposed framework disentangles general knowledge and task-specific adaptations . it generates residual modules that focus more exclusively on task-relevant information . |
How Do Lexical Senses Correspond Between Spoken German and German Sign Language? (2026.eacl-srw)
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| Challenge: | Existing dictionaries do not capture the full range of polysemous and homonymous words corresponding to different signs across contexts. |
| Approach: | They analyze 1,404 word use–to–sign ID mappings from German and German Sign Language . they identify three correspondence types: Type 1 (one-to-many), Type 2 (many-to-1), and Type 3 (one to one) |
| Outcome: | The proposed method outperforms existing methods using Exact Match and Semantic Similarity. |
A Lifelong Multilingual Multi-granularity Semantic Alignment Approach via Maximum Co-occurrence Probability (2024.lrec-main)
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| Challenge: | Existing methods to mask and predict tokens in multilingual text limit multilingual interaction . |
| Approach: | They propose a lifelong multilingual multi-granularity semantic alignment approach which continuously extracts massive aligned linguistic units from noisy data via a maximum co-occurrence probability algorithm. |
| Outcome: | The proposed approach improves translation performance on WMT14 18 benchmarks in twelve directions. |
Improving Context Modeling in Neural Topic Segmentation (2020.aacl-main)
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| Challenge: | Recent work favors highly effective neural supervised approaches for topic segmentation but current neural solutions are limited in how they model context. |
| Approach: | They propose to enhance a hierarchical attention biLSTM network-based topic segmenter to better model context by adding a coherence-related auxiliary task and restricted self-attention. |
| Outcome: | The proposed model outperforms SOTA approaches on three datasets and on four real-world benchmarks. |
An Information-Theoretic Approach and Dataset for Probing Gender Stereotypes in Multilingual Masked Language Models (2022.findings-naacl)
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| Challenge: | Pretrained language models (PLMs) have been shown to encapsulate social biases, including those relating to gender and race. |
| Approach: | They propose a new bias measure based on Jensen–Shannon divergence that retains more information from the model output probabilities than other previously proposed bias measures. |
| Outcome: | The proposed measure outperforms CrowS-Pairs and other similar measures for non-English datasets. |
MuLVE, A Multi-Language Vocabulary Evaluation Data Set (2022.lrec-1)
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| Challenge: | Existing systems for vocabulary evaluation are based on simple rules and do not account for real-life user learning data. |
| Approach: | They propose to use real-life user vocabulary learning data to evaluate vocabulary . they use language learning data from a phase6 vocabulary trainer to generate a multilingual data set for vocabulary evaluation. |
| Outcome: | The proposed data set provides outstanding results with 95.5 accuracy and F2-score. |
LeSpell - A Multi-Lingual Benchmark Corpus of Spelling Errors to Develop Spellchecking Methods for Learner Language (2022.lrec-1)
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| Challenge: | Existing spellcheckers do not work well with learner data. |
| Approach: | They propose a multi-lingual evaluation data set of spelling mistakes in context that is highly customizable for the DKPro architecture. |
| Outcome: | The proposed spellchecker improves performance in many settings and can be customized to meet learners' needs. |
Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem (2025.coling-main)
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| Challenge: | Natural language explanations (NLEs) are vital for elucidating the reasoning behind large language model (LLM) decisions. |
| Approach: | They propose a role-modeling approach that employs two LLMs as generator and critic to generate and refine NLEs. |
| Outcome: | The proposed model outperforms self-refine and can perform with less powerful LLMs. |
Exploring Cross-sentence Contexts for Named Entity Recognition with BERT (2020.coling-main)
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| Challenge: | Named entity recognition (NER) is often addressed as a sequence classification task with each input consisting of one sentence of text. |
| Approach: | They propose a method to combine different predictions from multiple sentences in input samples to increase NER performance. |
| Outcome: | The proposed method improves on the state-of-the-art NER results on English, Dutch, and Finnish and achieves the best reported BERT-based results on German. |
The EuroPat Corpus: A Parallel Corpus of European Patent Data (2022.lrec-1)
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| Challenge: | a new corpus of patent-specific parallel data is available for 6 official European languages paired with English: German, Spanish, French, Croatian, Norwegian, and Polish. |
| Approach: | They present a patent-specific corpus of parallel data for 6 official European languages paired with English: German, Spanish, French, Croatian, Norwegian, and Polish. |
| Outcome: | The filtered corpus ranges in size from 51 million sentences (Spanish-English) to 154k sentences (Croatian-English), with the unfiltered (raw) corpus being up to 2 times larger. |
MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder (2025.acl-industry)
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Khai Le-Duc, Phuc Phan, Tan-Hanh Pham, Bach Phan Tat, Minh-Huong Ngo, Thanh Nguyen-Tang, Truong-Son Hy
| Challenge: | Multilingual automatic speech recognition (ASR) in the medical domain is a critical foundational task, serving a wide range of downstream applications such as speech translation, spoken language understanding, and voice-activated assistants. |
| Approach: | They present the first multilingual medical ASR dataset and the first collection of small-to-large end-to end medical APR models spanning five languages: Vietnamese, English, German, French, and Mandarin Chinese. |
| Outcome: | The proposed model covers Vietnamese, English, German, French, and Mandarin Chinese, and is the first multilingual ASR dataset across five languages. |
IR like a SIR: Sense-enhanced Information Retrieval for Multiple Languages (2021.emnlp-main)
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Rexhina Blloshmi, Tommaso Pasini, Niccolò Campolungo, Somnath Banerjee, Roberto Navigli, Gabriella Pasi
| Challenge: | Recent advances in contextualized embeddings have made ranking on non-English documents cumbersome . a novel multilingual query expansion mechanism provides sense definitions as additional semantic information for the query. |
| Approach: | They propose a multilingual query expansion mechanism that leverages word sense information to enhance the model's performance. |
| Outcome: | The proposed model performs better than its supervised and unsupervised alternatives across languages while being trained on English Robust04 data. |
Sentiment Reasoning for Healthcare (2025.acl-industry)
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| Challenge: | Sentiment Reasoning is an auxiliary task in sentiment analysis where the model predicts both the sentiment label and generates the rationale behind it based on the input transcript. |
| Approach: | They propose a task - Sentiment Reasoning - for both speech and textmodalities and propose 'multimodal multitask framework' . they propose to use a model that generates the rationale behind each predicted label and provides a rationale for model prediction with quality semantically comparable to humans. |
| Outcome: | The proposed task improves model transparency by providing rationale for model prediction with quality semantically comparable to humans while improving model’s classification performance. |
Morphology Without Borders: Clause-Level Morphology (2022.tacl-1)
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| Challenge: | Morphological tasks use large multi-lingual datasets that organize words into inflection tables . lack of a clear linguistic and operational definition of what is a word impairs universality of tasks . |
| Approach: | They propose to view morphology as a clause-level phenomenon, rather than word-level . they propose to use a dataset for clause- level morphological tasks in 4 different languages . |
| Outcome: | The proposed dataset for clause-level morphology covers 4 typologically different languages: English, German, Turkish, and Hebrew. |
Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection (2020.findings-emnlp)
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| Challenge: | Recent studies have demonstrated the effectiveness of cross-lingual language model pre-training on NLP tasks. |
| Approach: | They propose a hybrid emoji-based Masked Language Model to leverage eojis across languages to improve the learning of short text messages. |
| Outcome: | The proposed model performs better on German, Italian and Spanish. |
GerMedIQ: A Resource for Simulated and Synthesized Anamnesis Interview Responses in German (2025.acl-srw)
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Justin Hofenbitzer, Sebastian Schöning, Belle Sebastian, Jacqueline Lammert, Luise Modersohn, Martin Boeker, Diego Frassinelli
| Challenge: | Text corpora in non-English clinical contexts is scarce due to privacy restrictions and restricted access to secure environments. |
| Approach: | They propose to use Large Language Models to generate synthetic data using a German medical interview questions corpus. |
| Outcome: | The proposed dataset generates comparable responses to human-generated questions. |
ParCorFull2.0: a Parallel Corpus Annotated with Full Coreference (2022.lrec-1)
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| Challenge: | Existing corpus ParCorFull contains parallel texts for English-German, French and Portuguese . translation of coreference across languages is challenging for MT and other NLP applications . |
| Approach: | They describe a parallel corpus annotated with full coreference chains for multiple languages . they use the existing corpus ParCorFull to study translation of coreference across languages - a challenge for machine translation and NLP . |
| Outcome: | The proposed corpus addresses translation of coreference across languages, a problem still challenging for machine translation and other multilingual natural language processing applications. |
KRAUTS: A German Temporally Annotated News Corpus (L18-1)
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| Challenge: | Temporal tagging is an important task towards improved natural language understanding. |
| Approach: | They present a new German temporally annotated corpus with 192 documents with 1,140 annotations . they propose to make temporal tagging a viable research area . |
| Outcome: | The proposed corpus contains 192 documents with 1,140 annotated temporal expressions. |
Context-Aware Text Normalisation for Historical Dialects (2020.coling-main)
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| Challenge: | Context-aware historical text normalisation is a severely under-researched area . a new approach to normalise historical spellings relies on the state-of-the-art methods . |
| Approach: | They propose a multidialect normaliser with a context-aware reranking approach . they incorporate dialectal information into the training and use a word-level n-gram language model . |
| Outcome: | The proposed approach improves accuracy on historical datasets and further improves on baseline. |
Our kind of people? Detecting populist references in political debates (2023.findings-eacl)
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| Challenge: | Existing literature on populism has only limited agreement on its exact properties . |
| Approach: | They propose a cross-lingual dataset to identify populist rhetoric in text . they propose 'hierarchical' annotation procedure to annotate populist references . |
| Outcome: | The proposed dataset can be used to investigate how political actors talk about The Elite and The People and to study how populist rhetoric is used as a strategic device. |
Quantifying Synthesis and Fusion and their Impact on Machine Translation (2022.naacl-main)
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| Challenge: | Literature in Natural Language Processing (NLP) typically labels whole language with strict type of morphology, e.g. fusional or agglutinative. |
| Approach: | They propose to quantify morphological typology at the word and segment level by using two indices: synthesis (e.g. analytic to polysynthetic) and fusion (agglutinative to fusional). |
| Outcome: | The proposed method reduces the rigidity of NLP classification claims by measuring morphological diversity at the word and segment level. |
Semi-Supervised Semantic Role Labeling with Cross-View Training (D19-1)
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| Challenge: | Recent approaches rely on expensive annotations and are unavailable in low resource scenarios (e.g., rare languages or domains). |
| Approach: | They propose an end-to-end SRL model which leverages unlabeled data and propose to reduce the annotation effort involved via semi-supervised learning. |
| Outcome: | The proposed model outperforms the state-of-the-art in English and consistently improves performance in other languages, including Chinese, German, and Spanish. |
The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text (2022.lrec-1)
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| Challenge: | a corpus of over 200,000 microblog translations supports translation of thirteen languages into English . large collections of parallel text, or bitext, are increasingly available in many languages . |
| Approach: | They propose a corpus of over 200,000 microblog posts that supports translation of thirteen languages into English. |
| Outcome: | The proposed corpus contains over 200,000 translations of microblog posts in 13 languages . fine-tuning showed significant improvements in translation quality . |
MuSeCLIR: A Multiple Senses and Cross-lingual Information Retrieval Dataset (2022.coling-1)
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| Challenge: | Existing datasets for cross-lingual information retrieval (CLIR) are dominated by searches for named entity mentions, which does not provide a good measure for disambiguation performance. |
| Approach: | They propose a dataset to evaluate CLIR systems' disambiguation ability based on polysemous common nouns with multiple possible translations. |
| Outcome: | The proposed dataset shows that it has a higher requirement on the ability of CLIR systems to disambiguate query terms. |
Multilingual Multi-class Sentiment Classification Using Convolutional Neural Networks (L18-1)
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| Challenge: | a new language-independent model for sentiment analysis is proposed for social media . a sentiment dictionary cannot list all the possible ways people can express their opinions . |
| Approach: | They propose a language-independent model for multi-class sentiment analysis using a neural network architecture. |
| Outcome: | The proposed model does not rely on language-specific features such as ontologies, dictionaries, or morphological or syntactic pre-processing. |
Cross-Lingual Abstract Meaning Representation Parsing (N18-1)
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| Challenge: | Abstract Meaning Representation (AMR) research has focused on English . Qualitative analysis shows that the new parsers overcome structural differences between the languages. |
| Approach: | They propose to use an AMR parser for English and parallel corpora to learn AMR for Italian, Spanish, German and Chinese. |
| Outcome: | The proposed method overcomes structural differences between the target languages and requires no gold standard data. |
Unveiling Dual Quality in Product Reviews: An NLP-Based Approach (2025.acl-industry)
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| Challenge: | Dual quality is a problem where products with identical ingredients or characteristics are sold under the same brand and similar packaging in different markets, but are significantly altered in composition or quality parameters. |
| Approach: | They propose to use natural language processing to detect inconsistent product quality by analyzing a Polish-language dataset and using different approaches. |
| Outcome: | The proposed approach can detect and address inconsistent product quality in Polish and other languages. |
Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models (2022.findings-acl)
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| Challenge: | Sequence-to-sequence models fail to generalize in hierarchy-sensitive manner when performing syntactic transformations. |
| Approach: | They evaluate whether seq2seq models generalize hierarchically on two transformations . they use pre-trained models and their multilingual variants to test their generalization . |
| Outcome: | The proposed models generalize hierarchically on two transformations in English and German. |
LLäMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratch (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have achieved remarkable success, yet this progress is predominantly centered on English. |
| Approach: | They create two German-only decoder models from scratch and publish them for the (German) NLP research community to use. |
| Outcome: | The two models performed competitively on the German SuperGLEBer benchmark, but performance improvements plateaued early during training, offering valuable insights into resource allocation for future models. |
Temporal Attention for Language Models (2022.findings-naacl)
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| Challenge: | Pretrained language models are trained on corpora derived from the web, but ignore this information. |
| Approach: | They propose a time-aware self-attention mechanism that captures time-specific contextualized word representations and allows the transformer to capture this information. |
| Outcome: | The proposed model achieves state-of-the-art on three datasets in different languages (English, German, and Latin) that vary in time, size, and genre. |
SMHD-GER: A Large-Scale Benchmark Dataset for Automatic Mental Health Detection from Social Media in German (2023.findings-eacl)
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| Challenge: | Mental health problems are a challenge to our modern society, and their prevalence is predicted to increase worldwide. |
| Approach: | They propose a large-scale, carefully constructed dataset for MHC detection built on high-precision patterns and the approach proposed for English. |
| Outcome: | The proposed model leverages engineered (psycho-)linguistic features as well as BERT-German to facilitate further research and conduct extensive experiments. |
DEbateNet-mig15:Tracing the 2015 Immigration Debate in Germany Over Time (2020.lrec-1)
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Gabriella Lapesa, Andre Blessing, Nico Blokker, Erenay Dayanik, Sebastian Haunss, Jonas Kuhn, Sebastian Padó
| Challenge: | a dataset for germany covering the public debate on immigration is annotated . a political science notion of a claim is used to represent the political discourse . |
| Approach: | They annotate a dataset for german public debate on immigration in 2015 using a political science notion of a claim . they identify claims in newspaper articles, assign them to actors and fine-grained categories and annotize their polarity and date. |
| Outcome: | The dataset is annotated by a political science framework and shows it captures political debate . it shows that political actors can change their positions and take a strong stand against them . |
Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers (2023.findings-eacl)
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| Challenge: | Existing studies show that incorporating demographic factors in language representations improves performance on downstream NLP tasks. |
| Approach: | They use continuous language modeling and dynamic multi-task learning to adapt pre-trained Transformers to incorporate demographic information into their representations. |
| Outcome: | The proposed model shows that the results are consistent with previous studies. |
Document Context Neural Machine Translation with Memory Networks (P18-1)
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| Challenge: | Experimental results show that our model exploits both source and target document context. |
| Approach: | They propose a document-level neural machine translation model which takes both source and target document context into account using memory networks. |
| Outcome: | The proposed model outperforms previous work in terms of BLEU and METEOR in English translations. |
Language Resources for Historical Newspapers: the Impresso Collection (2020.lrec-1)
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| Challenge: | digitization efforts are slowly but steadily contributing an increasing amount of facsimiles of cultural heritage documents. |
| Approach: | They propose to use a collection of newspaper data sets composed of text and image resources, curated and published within the context of the ‘impresso - Media Monitoring of the Past’ project. |
| Outcome: | The aim of the impresso resource collection is to contribute to historical language resources, and strengthen approaches to non-standard inputs and foster efficient processing of historical documents. |
Robustness Evaluation of the German Extractive Question Answering Task (2025.coling-main)
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| Challenge: | Existing evaluation benchmarks for Question Answering systems only include EM and F1 scores, but they overlook critical factors for the deployment of QA systems. |
| Approach: | They propose to define an evaluation method specifically tailored to the German language to evaluate the robustness of German QA models. |
| Outcome: | The proposed method extends existing methods to German language . it shows that all models are vulnerable to character-level perturbations . |
EPIC UdS - Creation and Applications of a Simultaneous Interpreting Corpus (2022.lrec-1)
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| Challenge: | EPIC UdS is a multilingual corpus of simultaneous interpreting for English, German and Spanish. |
| Approach: | They describe the creation and annotation of EPIC UdS, a multilingual corpus of simultaneous interpreting for English, German and Spanish. |
| Outcome: | The proposed corpus includes transcripts suitable for research on more than one language pair and on interpreting with regard to German. |
Building Literary Corpora for Computational Literary Analysis - A Prototype to Bridge the Gap between CL and DH (L18-1)
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| Challenge: | Literature analysis using corpus-based literary analysis is slow, says aaron s. e. . literary studies researchers should focus on the research practices of literary studies, he says . |
| Approach: | et al. show litText can extract text from a 20 million word corpus using SPARQL queries. |
| Outcome: | The proposed method uses a 20 million word corpus from English, German, Spanish, French and Italian texts and an example query to identify texts where animals behave like humans as it is the case in fables. |
Shallow Discourse Parsing for Under-Resourced Languages: Combining Machine Translation and Annotation Projection (2020.lrec-1)
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| Challenge: | Shallow Discourse Parsing (SDP) relies on large amounts of training data, which so far exists only for English. |
| Approach: | They propose to translate an existing English Penn Discourse TreeBank into German and use it to create a German corpus annotated for shallow discourse relations in the news domain. |
| Outcome: | The proposed corpus is annotated for shallow discourse relations in the (financial) news domain. |
DETECT: Determining Ease and Textual Clarity of German Text Simplifications (2026.eacl-long)
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| Challenge: | Current evaluation of German automatic text simplification relies on general-purpose metrics such as SARI, BLEU, and BERTScore. |
| Approach: | They propose a German-specific metric that holistically evaluates ATS quality across all three dimensions of simplicity, meaning preservation, and fluency. |
| Outcome: | The proposed metric achieves higher correlations with human judgments than widely used ATS metrics. |
Speaking Multiple Languages Affects the Moral Bias of Language Models (2023.findings-acl)
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Katharina Haemmerl, Bjoern Deiseroth, Patrick Schramowski, Jindřich Libovický, Constantin Rothkopf, Alexander Fraser, Kristian Kersting
| Challenge: | Pre-trained multilingual language models are often better on English than other languages . however, they are trained on varying amounts of data for each language . |
| Approach: | They apply the MORALDIRECTION framework to multilingual models and analyse their results . they find that PMLMs encode differing moral biases, but these do not correspond to cultural differences or commonalities in human opinions. |
| Outcome: | The proposed model captures moral norms from English and imposes them on other languages. |
Joint Learning of Syntactic Features Helps Discourse Segmentation (2020.lrec-1)
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| Challenge: | Discourse segmentation is a task of fragmenting text into minimal disjoint chunks of text called Elementary Discourse Units (EDUs). |
| Approach: | They propose a framework for multi-lingual discourse segmentation with BERT . they cast the problem as a token classification problem and jointly learn syntactic features like part-of-speech tags and dependency relations. |
| Outcome: | Experiments in English, Dutch, German, Portuguese Brazilian and Basque show that the proposed model performs better across languages. |
XL-LEXEME: WiC Pretrained Model for Cross-Lingual LEXical sEMantic changE (2023.acl-short)
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| Challenge: | Existing approaches to the Word in Context task use cross-encoders, which prevent the possibility of deriving comparable word embeddings. |
| Approach: | They propose a Lexical Semantic Change Detection model that extends SBERT, highlighting the target word in the sentence. |
| Outcome: | The proposed model outperforms the state-of-the-art on the multilingual benchmarks for SemEval-2020 Task 1 - Lexical Semantic Change (LSC) Detection and the RuShiftEval shared task. |
GeCzLex: Lexicon of Czech and German Anaphoric Connectives (2020.lrec-1)
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| Challenge: | Existing lexicons of connectives are interlinked with each other to provide a bilingual inventory of connective entries. |
| Approach: | They introduce the first version of a lexicon for translation equivalents of Czech and German discourse connectives. |
| Outcome: | The lexicon is the first bilingual inventory of connectives with linkage on the level of individual entries. |
Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization (N18-1)
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| Challenge: | a proposed model for generating natural language descriptions is too generic and does not exploit task specific characteristics. |
| Approach: | They propose a model which uses a fused bifocal attention mechanism to exploit micro and macro level information and a gated orthogonalization mechanism to ensure that a field is remembered for a few time steps and then forgotten. |
| Outcome: | The proposed model improves on a recently released dataset with two similar datasets for French and German. |
CMU-MOSEAS: A Multimodal Language Dataset for Spanish, Portuguese, German and French (2020.emnlp-main)
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AmirAli Bagher Zadeh, Yansheng Cao, Simon Hessner, Paul Pu Liang, Soujanya Poria, Louis-Philippe Morency
| Challenge: | Existing datasets in multimodal language are limited and disproportionately affect native speakers of other languages . authors propose a large-scale dataset for Spanish, Portuguese, German and French . |
| Approach: | They propose a large-scale multimodal language dataset for Spanish, Portuguese, German and French. |
| Outcome: | The proposed dataset is the largest of its kind with 40,000 total labelled sentences . it covers a diverse set topics and speakers and carries supervision of 20 labels including sentiment, emotions, and attributes. |
Making a Semantic Event-type Ontology Multilingual (2022.lrec-1)
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| Challenge: | a new version of SynSemClass is being developed for use in natural language processing . the ontology is a bilingual resource with no links to a valency lexicon . |
| Approach: | They propose to add German entries to the SynSemClass Event-type Ontology . they propose to use the ontology as a human-readable and human-understandable database . |
| Outcome: | The proposed extension of SynSemClass Event-type Ontology is presented in a paper in czech republic . the ontology provides curated data for NLP experiments with cross-lingual synonyms . |
Cross-lingual Contextualized Topic Models with Zero-shot Learning (2021.eacl-main)
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| Challenge: | Existing topic models are language-specific and cannot be transferred in a transferable manner. |
| Approach: | They propose a zero-shot cross-lingual topic model that learns topics on one language and predicts them for unseen documents in different languages. |
| Outcome: | The proposed model learns topics on one language and predicts them for unseen documents in different languages. |
Polar Quantification of Actor Noun Phrases for German (2022.lrec-1)
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| Challenge: | Polanyi and Zaenen (2006) focused on the negative polar load of noun phrases, especially those denoting actors. |
| Approach: | They propose a method to measure the negative polar load of noun phrases by using a silver standard and a BERT-based intensity regressor. |
| Outcome: | The proposed model is based on a lexicon-based silver standard and tested empirically. |
Multi-class Multilingual Classification of Wikipedia Articles Using Extended Named Entity Tag Set (2020.lrec-1)
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| Challenge: | Existing classification models struggle with large datasets using fine-grained tag sets. |
| Approach: | They propose to structure Wikipedia into a large multi-lingual dataset using an Extended Named Entity tag set. |
| Outcome: | The proposed model fails to describe why Wikipedia articles are used to summarize, translate or answer questions. |
Multilingual Multifaceted Understanding of Online News in Terms of Genre, Framing, and Persuasion Techniques (2023.acl-long)
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| Challenge: | a new dataset of news articles is presented that covers genre, framing, and persuasion techniques. |
| Approach: | They propose a multilingual multifacet dataset of news articles annotated for genre, framing and persuasion techniques. |
| Outcome: | The proposed dataset contains 1,612 news articles covering recent news on current topics of public interest in six European languages. |
Generating Extended and Multilingual Summaries with Pre-trained Transformers (2022.lrec-1)
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| Challenge: | Almost all summarisation methods focus on a single language and short summaries. |
| Approach: | They propose a dataset for extended summarisation tailored for 11 sentences . they compare three multilingual transformer models on extractive and abstractive summarization tasks . |
| Outcome: | The proposed dataset is tailored for extended summaries of approx. 11 sentences. |
The ApposCorpus: a new multilingual, multi-domain dataset for factual appositive generation (2020.coling-main)
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| Challenge: | appositives are phrases that appear next to a noun phrase and serve an explicative function. |
| Approach: | They propose a more realistic end-to-end definition of appositive generation with a dataset that spans four languages and two entity types. |
| Outcome: | The proposed model is non-trivial and leaves plenty of room for improvement. |
It’s All About the Confidence: An Unsupervised Approach for Multilingual Historical Entity Linking using Large Language Models (2026.eacl-long)
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| Challenge: | Existing approaches to EL for historical texts require substantial training data or rely on domain-specific rules that limit scalability. |
| Approach: | They propose an unsupervised ensemble approach combining a Small Language Model and an LLM for historical EL. |
| Outcome: | The proposed approach outperforms state-of-the-art models on four established benchmarks in six European languages from the 19th and 20th centuries. |
MTG: A Benchmark Suite for Multilingual Text Generation (2022.findings-naacl)
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| Challenge: | Using MTG, we train and evaluate multilingual text generation models using human-annotated data. |
| Approach: | They propose a multilingual multiway text generation dataset with 400k human-annotated data that includes four generation tasks across five languages. |
| Outcome: | The proposed dataset includes four generation tasks across five languages (English, German, French, Spanish and Chinese) it provides comprehensive evaluations with diverse generation scenarios. |
XL-AMR: Enabling Cross-Lingual AMR Parsing with Transfer Learning Techniques (2020.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) is a popular formalism of natural language. |
| Approach: | They develop a cross-lingual AMR parser that can be trained on the produced data . they use transfer learning techniques to produce automatic AMR annotations across languages . |
| Outcome: | The proposed parser significantly surpasses those reported in Chinese, German, Italian and Spanish. |
Cross-lingual Visual Verb Sense Disambiguation (N19-1)
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| Challenge: | Recent work has shown that visual context improves cross-lingual sense disambiguation for nouns. |
| Approach: | They extend their work to the task of cross-lingual verb sense disambiguation by using a dataset annotated with English, German, and Spanish verbs. |
| Outcome: | The proposed model improves the results of a text-only machine translation system when used for a multimodal translation task. |
Automatically Creating a Lexicon of Verbal Polarity Shifters: Mono- and Cross-lingual Methods for German (C18-1)
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| Challenge: | a large number of verbal polarity shifters are available for multiple languages, but only English has a sizable lexicon of them. |
| Approach: | They use methods to create large lexicon of verbal polarity shifters in germany . they bootstrap annotated verbs with a supervised classifier and apply them to German . |
| Outcome: | The proposed method is able to create a large lexicon of verbal polarity shifters in germany . it reduces annotation effort by leveraging cross-lingual information from the English lexico . |
Bits and Pieces: Investigating the Effects of Subwords in Multi-task Parsing across Languages and Domains (2024.lrec-main)
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| Challenge: | Neural parsing is dependent on the underlying language model, but little is known about how choices affect parser performance. |
| Approach: | They examine how subword sharing is responsible for gains or negative transfer in multi-task learning . they find a preference for averaged or last subwords across languages and domains . |
| Outcome: | The proposed model favors averaged or last subwords across languages and domains . specific POS tags may require different subword, and distribution overlap is more important than discrepancies in the data sizes. |
Linguistic Appropriateness and Pedagogic Usefulness of Reading Comprehension Questions (2020.lrec-1)
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| Challenge: | Existing evaluation measures for automatic generation of reading comprehension questions focus on linguistic quality only, ignoring educational value and appropriateness of questions. |
| Approach: | They propose a new evaluation scheme where questions are structured in a hierarchical way . they also create and evaluate two new evaluation data sets for Basque and German . |
| Outcome: | The proposed evaluation scheme can be applied, but expert annotators are needed. |
Open Subtitles Paraphrase Corpus for Six Languages (L18-1)
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| Challenge: | Opusparcus is a new corpus of paraphrases for six European languages . it is based on movie and TV subtitles, which are colloquial and informal . |
| Approach: | They propose to use opensubtitles2016 paraphrase corpus for six European languages . they extract paraphrases from movie and TV subtitles from the corpus . |
| Outcome: | The new corpus is available in German, English, Finnish, French, Russian, and Swedish . it is extracted from the OpenSubtitles2016 corpus, which contains subtitles from movies and TV shows . |
Span-based discontinuous constituency parsing: a family of exact chart-based algorithms with time complexities from O(nˆ6) down to O(nˆ3) (2020.emnlp-main)
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| Challenge: | a novel chart-based parser for discontinuous constituency trees is proposed for span-based span parsing . it can process discontinuous constituent trees of block degree two, including ill-nested structures . |
| Approach: | They propose a chart-based algorithm for span-based parsing of discontinuous constituency trees . they build variants with smaller search spaces and time complexities ranging from O(n6) down to O(N3) . |
| Outcome: | The proposed algorithm can process 98% of constituents in linguistic treebanks while having the same complexity as continuous constituency parsers. |
Paraphrase Generation and Evaluation on Colloquial-Style Sentences (2020.lrec-1)
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| Challenge: | a new study investigates the quality and novelty of generated paraphrases . paraphrase models can be used for information retrieval and data mining . |
| Approach: | They use state-of-the-art neural machine translation models trained on the Opusparcus corpus to generate paraphrases in six languages. |
| Outcome: | The proposed model outperforms the existing model on human evaluation in five of the six languages. |
Robustness of Multi-Source MT to Transcription Errors (2023.findings-acl)
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| Challenge: | In multilingual settings, the same content may be available in various languages via simultaneous interpreting, dubbing or subtitling. |
| Approach: | They hypothesize that leveraging multiple sources will improve translation quality if the sources complement one another in terms of correct information they contain. |
| Outcome: | The proposed method is robust to speech recognition errors on a 10-hour ESIC corpus. |
Attention on Multiword Expressions: A Multilingual Study of BERT-based Models with Regard to Idiomaticity and Microsyntax (2025.findings-naacl)
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| Challenge: | Specifically, models fine-tuned on semantic tasks tend to distribute attention to idiomatic expressions more evenly across layers. |
| Approach: | They analyze attention patterns of encoder-only models towards two distinct types of Multiword Expressions (MWEs) idioms present challenges in semantic non-compositionality, while MSUs demonstrate unconventional syntactic behavior that does not conform to standard grammatical categorizations. |
| Outcome: | The proposed models show that fine-tuned models allocate attention to idiomatic expressions more evenly across layers. |
Can Word Sense Distribution Detect Semantic Changes of Words? (2023.findings-emnlp)
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| Challenge: | Existing methods to detect semantic variations of words are not accurate for time-sensitive predictions. |
| Approach: | They propose to use pretrained static sense embeddings to annotate a word's occurrence with a sense id to compare its distributions. |
| Outcome: | The proposed method compares word sense distributions across two corpora to predict meaning change . the results show that pretrained LLMs can detect changes in words over time . |
Argument Similarity Assessment in German for Intelligent Tutoring: Crowdsourced Dataset and First Experiments (2022.lrec-1)
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| Challenge: | Recent years have seen increasing interest in applying natural language processing (NLP) applications to the field of education. |
| Approach: | They propose an NLP-based system that supports german secondary school students in an argumentative writing exercise. |
| Outcome: | The proposed system will support students in a German school exercise . the system will assess similarity between arguments in snippets of argumentative text . |
Multilingual AMR Parsing with Noisy Knowledge Distillation (2021.findings-emnlp)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is a broad-coverage semantic formalism that encodes the meaning of a sentence as a rooted, directed, and labeled graph. |
| Approach: | They propose to use existing English parser to learn and improve multilingual AMR parsers . their results show that noisy input and precise output are key to successful distillation . |
| Outcome: | The proposed model outperforms the current state-of-the-art English-only parser on four different languages. |
Can Factual Statements Be Deceptive? The DeFaBel Corpus of Belief-based Deception (2024.lrec-main)
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| Challenge: | if a person firmly believes in a non-factual statement, there is no inherent intention to deceive. |
| Approach: | They propose to use the DeFaBel corpus to study the relationship between deception and factuality based on belief to generate arguments supporting statements . |
| Outcome: | The DeFaBel corpus contains 1031 texts in german, out of which 643 are deceptive and 388 are non-deceptive. |
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks (D19-1)
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| Challenge: | Existing models that can handle cross-lingual tasks with limited or no training data are insensitive to different languages. |
| Approach: | They propose to use Unicoder to train models in one language and apply it to other languages. |
| Outcome: | Experiments show that Unicoder learns the mappings among different languages from more perspectives. |
XFUND: A Benchmark Dataset for Multilingual Visually Rich Form Understanding (2022.findings-acl)
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| Challenge: | Existing research on multimodal pre-training for visually rich document understanding tasks has focused on the English domain while neglecting the importance of multilingual generalization. |
| Approach: | They propose a multimodal pre-trained model for multilingual document understanding which bridges the language barriers for visually rich document understanding. |
| Outcome: | The proposed model outperforms existing cross-lingual pre-trained models on the XFUND dataset on visual document understanding tasks. |
Enlivening Redundant Heads in Multi-head Self-attention for Machine Translation (2021.emnlp-main)
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| Challenge: | Existing methods to improve multi-head self-attention are lacking in many languages. |
| Approach: | They propose a redundant head enlivening method to identify redundant heads and vitalize their potential by learning syntactic relations and prior knowledge in the text. |
| Outcome: | The proposed method can identify and vitalize redundant heads without sacrificing the roles of important heads. |
Tailoring Neural Architectures for Translating from Morphologically Rich Languages (C18-1)
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| Challenge: | A morphologically complex word is a hierarchical constituent with meaning-preserving subunits, so word-based models which rely on surface forms might not be powerful enough to translate such structures. |
| Approach: | They propose a neural architecture which is designed to deal with morphological complexities on the source side and redesign the decoder accordingly to benefit from such information. |
| Outcome: | The proposed model outperforms existing subword- and character-based architectures and showed significant improvements on translating from German, Russian, and Turkish into English. |
Sentence Compression for Arbitrary Languages via Multilingual Pivoting (D18-1)
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| Challenge: | a new study advocates the use of bilingual corpora for sentence compression models . previous work focused on word deletion, while others view sentence compression as a general text rewriting problem. |
| Approach: | They propose to use bilingual corpora for training sentence compression models. |
| Outcome: | The proposed model can be trained for any language as long as a bilingual corpus is available . it performs arbitrary rewrites without access to compression specific data . |
Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog (2022.naacl-main)
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| Challenge: | Task-oriented dialog (TOD) is arguably one of the most popular natural language processing (NLP) application areas. |
| Approach: | They propose a multilingual multi-domain TOD dataset that spans four languages . they use a framework for multilingual conversational specialization of pretrained language models . |
| Outcome: | The proposed datasets show that they perform better than existing datasets in English . the proposed framework allows for sample-efficient few-shot transfer for TOD tasks . |
Merkel Podcast Corpus: A Multimodal Dataset Compiled from 16 Years of Angela Merkel’s Weekly Video Podcasts (2022.lrec-1)
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| Challenge: | a dataset of 16 years of (almost) weekly Internet podcasts of former german chancellor Angela Merkel is presented. |
| Approach: | They propose to curate a German podcast corpus from 16 years of podcasts of former german chancellor Angela Merkel using audio-visual-text methods. |
| Outcome: | The proposed pipeline can be used to curate other datasets of similar nature, such as talk show contents. |
Summarising Historical Text in Modern Languages (2021.eacl-main)
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| Challenge: | Historical text summarisation is a routine for historians and digital humanities researchers but has never been automated. |
| Approach: | They propose a model that can be trained even with no cross-lingual data and further benchmark it against state-of-the-art algorithms. |
| Outcome: | The proposed model outperforms standard cross-lingual benchmarks on historical text summarisation task and identifies distinctness and value of the dataset. |
Dynamic Programming Encoding for Subword Segmentation in Neural Machine Translation (2020.acl-main)
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| Challenge: | Empirical results on machine translation suggest that DPE is effective for segmenting output sentences. |
| Approach: | They propose a new algorithm for tokenizing sentences into subword units . they propose enabling exact log marginal likelihood estimation and exact MAP inference . |
| Outcome: | The proposed algorithm improves on machine translation datasets and on a large dataset. |
Multilingual Content Moderation: A Case Study on Reddit (2023.eacl-main)
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| Challenge: | a growing need for AI moderators to safeguard users and protect mental health of human moderator from traumatic content. |
| Approach: | They propose to use a multilingual dataset to study the challenges of content moderation . they propose to analyze 1.8 million Reddit comments in English, german, spanish and french . |
| Outcome: | The proposed dataset highlights the challenges and suggests related research problems . it shows that the proposed model can be used to predict the violated rule . |
Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations (2024.acl-long)
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| Challenge: | Vision-and-language models with separate encoders for each modality are limited in availability. |
| Approach: | They propose a multilingual benchmark that offers (partial) translations of ImageNet labels to 100 languages, built without machine translation or manual annotation. |
| Outcome: | The proposed model outperforms models on English and low-resource languages. |
The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation (2021.findings-emnlp)
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| Challenge: | Extending state-of-the-art language models to low-resource languages requires addressing what we call the low-Resource double bind. |
| Approach: | They propose a low-resource double bind to refer to the co-occurrence of data limitations and compute resource constraints. |
| Outcome: | The proposed model improves performance on frequent sentences but disparates on infrequent ones. |
Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction (D18-1)
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| Challenge: | Recent attempts to improve grammar induction systems by bounding complexity of the model have not been compared against unbounded induction models. |
| Approach: | They propose to use a Bayesian induction inducer to limit the search space of the model and then sample trees with or without bounding. |
| Outcome: | The proposed model produces trees more accurately than or competitively with state-of-the-art constituency grammar induction models. |
WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models (2022.naacl-main)
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| Challenge: | Existing methods to train large pretrained language models require more computational resources and are expensive to train in other languages. |
| Approach: | They propose a method to transfer pretrained language models to new languages using subword-based tokenization and embeddings. |
| Outcome: | The proposed method outperforms existing methods on low-resource languages and makes training large models more accessible and less damaging to the environment. |
Embedding Space Correlation as a Measure of Domain Similarity (2020.lrec-1)
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| Challenge: | Existing work on domain similarity using text-based features of corpus is limited by pre-trained word embeddings. |
| Approach: | They propose a measure of domain similarity based on dimension-wise correlations between embedding spaces . they find a threshold at which the measure indicates that two corpora come from the same domain . |
| Outcome: | The proposed measure can be used to determine which corpora are more similar to each other in a cross-domain sentiment detection task. |
Exploring Multilingual Pre-trained Language Model for Aspect-based Sentiment Analysis (2026.findings-acl)
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| Challenge: | Aspect-based sentiment analysis studies have focused on English datasets, but labeled data is scarce. |
| Approach: | They propose a multilingual pre-trained language model that leverages bilingual pre-training to leverage aspects-based sentiment analysis. |
| Outcome: | The proposed model outperforms state-of-the-art models across multiple languages. |
DiBiMT: A Novel Benchmark for Measuring Word Sense Disambiguation Biases in Machine Translation (2022.acl-long)
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| Challenge: | Lexical ambiguity poses one of the greatest challenges in the field of Machine Translation. |
| Approach: | They propose a new benchmark to study semantic biases in Machine Translation of nominal and verbal words in five different languages. |
| Outcome: | The proposed benchmark tests state-of-the-art machine translation systems against the new test bed and provides a statistical and linguistic analysis of the results. |
MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models (2025.coling-main)
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| Challenge: | Existing studies on knowledge editing focus on monolingual scenarios, neglecting the complexities presented by multilingual contexts and multi-hop reasoning. |
| Approach: | They propose a benchmark to evaluate the adaptability of multilingual knowledge editing methods. |
| Outcome: | The proposed benchmark evaluates the adaptability of multilingual knowledge editing methods across five languages. |
FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing (2022.acl-long)
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| Challenge: | Using pre-trained language models, we evaluate performance group disparities while none of these techniques guarantee fairness, nor consistently mitigate group disparity. |
| Approach: | They present a benchmark suite of four datasets for evaluating the fairness of pre-trained language models and the techniques used to fine-tune them for downstream tasks. |
| Outcome: | The proposed methods show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparity. |
Multilingual Extension of PDTB-Style Annotation: The Case of TED Multilingual Discourse Bank (L18-1)
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| Challenge: | Existing corpora enriched with discourse annotations are scarce but exist . TED-MDB is hoped to be a source of parallel data for contrastive linguistic analysis and language technology applications. |
| Approach: | They propose a multilingual discourse treebank to provide a clear description of discourse structure and semantics in multiple languages. |
| Outcome: | The proposed corpus provides a clearly described level of discourse structure and semantics in multiple languages. |
BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation (2022.acl-long)
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| Challenge: | Existing benchmarks for OIE are incomplete and do not include all acceptable variants of the same fact. |
| Approach: | They introduce BenchIE: a benchmark and evaluation framework for comprehensive evaluation of OIE systems for English, Chinese, and German. |
| Outcome: | The proposed framework is based on fact synsets, clusters, and standardized benchmarks. |
A Corpus of German Citizen Contributions in Mobility Planning: Supporting Evaluation Through Multidimensional Classification (2022.lrec-1)
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| Challenge: | Political authorities in democratic countries consult the public in order to allow citizens to voice their ideas and concerns on specific issues. |
| Approach: | They propose a publicly-available corpus that includes citizen contributions from six mobility-related planning processes in five german municipalities. |
| Outcome: | The proposed corpus includes several thousand citizen contributions from six mobility-related planning processes in five German municipalities. |
Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects (2026.acl-long)
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| Challenge: | Research on cross-dialectal transfer from a standard to a non-standard dialect variety has typically focused on text data. |
| Approach: | They compare standard-to-dialect transfer in three settings: text models, speech models, and cascaded systems where speech first gets automatically transcribed and then further processed by a text model. |
| Outcome: | The proposed model performs best on German dialect data while the text-only model perform best on the standard data. |
Examining and Adapting Time for Multilingual Classification via Mixture of Temporal Experts (2025.naacl-long)
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| Challenge: | Existing classification models only consider the temporal variations of existing data . current models focus on English corpora, leaving time as domains unexplored . |
| Approach: | They propose a framework to generalize classifiers over time on four languages, English, Danish, French, and German. |
| Outcome: | The proposed framework can generalize classifiers over time on four languages, English, Danish, French, and German. |
Supporting Cognitive and Emotional Empathic Writing of Students (2021.acl-long)
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| Challenge: | Empathy skills are an elementary skill in society for daily interaction and professional communication and are therefore elementary for educational curricula. |
| Approach: | They propose an annotation approach to capture emotional and cognitive empathy in student-written peer reviews on business models in germany. |
| Outcome: | The proposed annotation scheme guides annotators to a substantial to moderate agreement with the model and shows that it is effective. |
Are Pretrained Multilingual Models Equally Fair across Languages? (2022.coling-1)
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| Challenge: | Pretrained multilingual language models can help bridge the digital language divide, enabling high-quality NLP models for lower-resourced languages. |
| Approach: | They propose to use a multilingual dataset to examine whether multilingual models are equally fair across languages. |
| Outcome: | The proposed model enables apples-to-apples comparison across languages of group disparities in multilingual language models. |
X-SRL: A Parallel Cross-Lingual Semantic Role Labeling Dataset (2020.emnlp-main)
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| Challenge: | Existing multilingual SRL datasets contain disparate annotation styles or come from different domains, hampering generalization in multilingual learning. |
| Approach: | They propose to automatically construct an SRL corpus that is parallel in four languages with unified predicate and role annotations that are fully comparable across languages. |
| Outcome: | The proposed method improves performance for English SRL in weaker languages. |
Customizing Grapheme-to-Phoneme System for Non-Trivial Transcription Problems in Bangla Language (N19-1)
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Sudipta Saha Shubha, Nafis Sadeq, Shafayat Ahmed, Md. Nahidul Islam, Muhammad Abdullah Adnan, Md. Yasin Ali Khan, Mohammad Zuberul Islam
| Challenge: | Existing methods for Grapheme to phoneme conversion in Bangla language are mostly rule-based. |
| Approach: | They propose to use a lexicon to train a robust Grapheme to phoneme conversion system in Bangla language. |
| Outcome: | The proposed method outperforms other state-of-the-art approaches for G2P conversion in Bangla language. |
Dynamically Adjusting Transformer Batch Size by Monitoring Gradient Direction Change (2020.acl-main)
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| Challenge: | Compared to previous studies, the performance of neural models is likely to be affected by the choice of hyper-parameters. |
| Approach: | They propose to automatically and dynamically determine batch sizes by accumulating gradients of mini-batches and performing an optimization step at just the time when the direction of gradients starts to fluctuate. |
| Outcome: | The proposed approach improves the Transformer model with a fixed 25k batch size by +0.73 and +0.82 BLEU respectively. |
BPE-knockout: Pruning Pre-existing BPE Tokenisers with Backwards-compatible Morphological Semi-supervision (2024.naacl-long)
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| Challenge: | Subword tokenisation is a mainstay in natural language processing (NLP) it allows the representation of an infinite space of text with a finite set of units. |
| Approach: | They propose to use byte-pair encoding to represent an infinite space of text with a finite set of units by removing subwords from the BPE vocabulary without impeding further use of merges that relied on them. |
| Outcome: | The proposed method improves token-based tasks in English, Dutch and German while saving training costs. |
Metrical Tagging in the Wild: Building and Annotating Poetry Corpora with Rhythmic Features (2021.eacl-main)
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| Challenge: | a prerequisite for the computational study of literature is the availability of properly digitized texts with reliable meta-data and ground-truth annotation. |
| Approach: | They propose to annotate prosodic features in large poetry corpora for English and German and train corpus driven neural models that enable large scale analysis. |
| Outcome: | The proposed models outperform baseline and BERT-based approaches in English and german and show that they learn foot boundaries better when jointly predicting syllable stress, aesthetic emotions and verse measures benefit from each other. |
Using Semantic Role Labeling to Improve Neural Machine Translation (2022.lrec-1)
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| Challenge: | despite progress in machine translation, some form of language understanding may be desirable . current systems rely on pattern recognition, but some form may be useful . |
| Approach: | They use semantic role labeling to annotate a standard parallel corpus with semantic roles . they then train a neural machine translation system using the annotated corpus and original unannotated text . |
| Outcome: | The proposed system improves BLEU scores for English, French, German, Greek and Spanish. |
A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference (2022.lrec-1)
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| Challenge: | Natural Language Inference (NLI) is a crucial task in AI and natural language processing. |
| Approach: | They propose an effective transfer learning approach for cross-lingual NLI . they perform experiments on English-Hindi language pairs in cross-linguistic setting . |
| Outcome: | The proposed model improves the baseline model by 10% over the state-of-the-art model. |
Constructing Korean Learners’ L2 Speech Corpus of Seven Languages for Automatic Pronunciation Assessment (2024.lrec-main)
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| Challenge: | Multilingual L2 speech corpora for automatic speech assessment are currently available, but lack comprehensive annotations of L2 from non-native speakers of various languages. |
| Approach: | They propose to use Korean learners’ L2 speech corpus of seven languages to develop automatic speech assessment. |
| Outcome: | The proposed corpus contains 1,200 hours of L2 speech data from Korean learners (400 hours for English, 200 hours each for Japanese and Chinese, 100 hours each in French, German, Spanish, and Russian). |
GeCoTagger: Annotation of German Verb Complements with Conditional Random Fields (L18-1)
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| Challenge: | Complement phrases are essential for constructing well-formed sentences in German. |
| Approach: | They propose an algorithm which can identify and classify complement phrases of any German verb in any written sentence context. |
| Outcome: | The proposed algorithm can identify and classify complement phrases of any German verb in any written sentence context. |
Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study (2023.emnlp-main)
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| Challenge: | a network of doppelganger websites (impersonating genuine news sites) was discovered in 2022 . a novel dataset enables studies of disinformation networks and the training of NLP tools for disinformation detection. |
| Approach: | They analyze two hitherto unstudied sites sharing state-backed disinformation . they perform cross-site topic clustering and perform linguistic and temporal analysis . |
| Outcome: | The proposed dataset includes 14,053 articles, annotated with each language version, and additional metadata such as links and images. |
A 2nd Longitudinal Corpus for Children’s Writing with Enhanced Output for Specific Spelling Patterns (L18-1)
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| Challenge: | IQB study looks at reading, mathematics and spelling ability across different states. |
| Approach: | They collect three longitudinal corpora of German school children's weekly writing in German and transcribe them into a corpus for research via Linguistic Data Consortium. |
| Outcome: | The corpus of German school children's weekly writing in German was collected and transcribed. |
Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions (2023.emnlp-main)
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| Challenge: | Currently, only 20% of the English comments explicitly mention content moderation policies, but as few as 2% of the German and Turkish comments. |
| Approach: | They propose to use a multilingual dataset to predict stances with existing content moderation policies and to use them to explain moderation decisions. |
| Outcome: | The proposed model predicts stances and corresponding reasons with high accuracy, adding transparency to the decision-making process. |
M2QA: Multi-domain Multilingual Question Answering (2024.findings-emnlp)
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| Challenge: | Language varies along several axes, most importantly, language instance and domain . lack of evaluation datasets prevents transfer of NLP systems to non-dominant languages . |
| Approach: | They propose a multi-domain multilingual question answering benchmark to explore cross-lingual cross-domain performance of fine-tuned models and state-of-the-art LLMs. |
| Outcome: | The proposed benchmark compared 13,500 SQuAD 2.0-style question-answer instances in German, Turkish, and Chinese for the domains of product reviews, news, and creative writing. |
Croatian Idioms Integration: Enhancing the LIdioms Multilingual Linked Idioms Dataset (2024.lrec-main)
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| Challenge: | Existing datasets that include idioms from English, German, Italian, Portuguese and Russian do not include a comprehensive representation of idiomatic expressions in Croatian. |
| Approach: | They propose to extend existing RDF-based multilingual representation of idioms to include 1,042 Croatian idiomes in an Ontolex Lemon format. |
| Outcome: | The proposed resource includes 1,042 Croatian idioms in an Ontolex Lemon format to foster translation initiatives and facilitate intercultural exchange. |
Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations (2022.coling-1)
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| Challenge: | Existing semantic parsers are based on deep learning, but rule-based approaches offer advantages . a drawback of neural semantic parses is that their output lacks explainability . |
| Approach: | They propose a method that maps a syntactic dependency tree to a formal meaning representation using a series of graph transformations. |
| Outcome: | The proposed method outperforms neural parsers in English, German, Italian and Dutch. |
The Multilingual Amazon Reviews Corpus (2020.emnlp-main)
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| Challenge: | The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019 . |
| Approach: | They propose to use mean absolute error (MAE) instead of classification accuracy for this task since MAE accounts for ordinal nature of the ratings. |
| Outcome: | The proposed model uses mean absolute error (MAE) instead of classification accuracy since MAE accounts for ordinal nature of the ratings. |
Using Wiktionary to Create Specialized Lexical Resources and Datasets (2022.lrec-1)
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| Challenge: | Using Wiktionary data to build specialized lexical datasets can be used for evaluating or improving NLP tasks, like Word Sense Disambiguation (WSD), Word-in-Context challenges (WiC), or Machine Translation (MT). |
| Approach: | They propose to use Wiktionary data to create specialized lexical datasets that can be used for evaluating or improving NLP tasks. |
| Outcome: | The proposed datasets can be used to improve and/or evaluate NLP tasks, like Word Sense Disambiguation (WSD), Word-in-Context challenges (WiC), or Sense Linking (SL), or machine translation (MT). |
Preserving Semantic Information from Old Dictionaries: Linking Senses of the ‘Altfranzösisches Wörterbuch’ to WordNet (2020.lrec-1)
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| Challenge: | Historical dictionaries of the pre-digital period are important resources for the study of older languages. |
| Approach: | They propose to use printed dictionaries to create a more easily accessible and more sustainable lexical database by automating the conversion process. |
| Outcome: | The ‘Altfranzösisches Wörterbuch’, an Old French dictionary published from 1925 onwards, shows how the printed dictionaries can be turned into a more easily accessible and more sustainable lexical database. |
Assessing Human-Parity in Machine Translation on the Segment Level (2020.findings-emnlp)
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| Challenge: | Recent machine translation shared tasks have shown top-performing systems to tie or outperform human translation. |
| Approach: | They examine the outputs of top-performing systems in a recent machine translation shared task . they find that some systems outperform human translation on average . |
| Outcome: | a new method identifies segments for which human and machine perform poorly . the results show that top-performing systems outperform human translation on average . |
Multichannel Generative Language Model: Learning All Possible Factorizations Within and Across Channels (2020.findings-emnlp)
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| Challenge: | MGLM is a generative joint distribution model over channels. |
| Approach: | They propose a multichannel generative joint distribution model over channels that marginalizes over all possible factorizations within and across all channels. |
| Outcome: | The proposed model outperforms traditional bilingual discriminative models. |
Challenge Dataset of Cognates and False Friend Pairs from Indian Languages (2020.lrec-1)
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| Challenge: | Cognates are words that have a common etymological origin and can facilitate the Second Language Acquisition (SLA) however, they also pose a challenge to various NLP applications such as Machine Translation and Cross-lingual Sense Disambiguation. |
| Approach: | They create two cognate datasets for twelve Indian languages and use them to generate cognate sets. |
| Outcome: | The proposed datasets are curated using previously available baseline cognate detection approaches and evaluated with the help of lexicographers. |
Neural Reranking for Dependency Parsing: An Evaluation (2020.acl-main)
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| Challenge: | Recent work shows that neural rerankers can improve dependency parsing results over the top k trees produced by a base parser. |
| Approach: | They propose to use a discriminative reranker to improve dependency parsing results . they propose to incorporate global information into the model to improve parse accuracies . |
| Outcome: | The proposed model outperforms existing models on English and German and Czech, and is the only one to improve on German and Chinese data. |
On the Multilingual Ability of Decoder-based Pre-trained Language Models: Finding and Controlling Language-Specific Neurons (2024.naacl-long)
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| Challenge: | Existing decoder-based pre-trained language models demonstrate excellent multilingual capabilities, but it is unclear how they handle multilingualism. |
| Approach: | They propose to examine the neuron-level internal behavior of decoder-based PLMs by finding neurons that fire “uniquely for each language” within decoded PLM models. |
| Outcome: | The proposed models fire “uniquely for each language” and show that language-specific neurons are unique, with a slight overlap (5%) between languages. |
LIdioms: A Multilingual Linked Idioms Data Set (L18-1)
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| Challenge: | Recent studies have focused on linguistic data sets that are bilingual on the Linguistic Linked Open Data (LLOD) 1 . |
| Approach: | They describe a multilingual RDF representation of idioms currently containing five languages . they use a model to structure the data and a method to link the data to well-known multilingual data sets such as BabelNet. |
| Outcome: | The proposed model complies with best practices according to Linguistic Linked Open Data Community. |
Maximum Bayes Smatch Ensemble Distillation for AMR Parsing (2022.naacl-main)
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| Challenge: | AMR parsing has experienced an unprecendented increase in performance in the last three years due to a mixture of effects including architecture improvements and transfer learning. |
| Approach: | They propose to combine Smatch-based ensembling techniques with ensemble distillation to overcome this diminishing returns of silver data. |
| Outcome: | The proposed technique can produce gains rivaling those of human annotated data for QALD-9 and achieve a new state-of-the-art for BioAMR. |
A Multilingual Test Collection for the Semantic Search of Entity Categories (L18-1)
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Juliano Efson Sales, Siamak Barzegar, Wellington Franco, Bernhard Bermeitinger, Tiago Cunha, Brian Davis, André Freitas, Siegfried Handschuh
| Challenge: | Despite the high popularity of entity search, entity categories have not received equal attention. |
| Approach: | They propose to make public a multilingual test collection comprehending English, Portuguese and German to meet the demands of the entity search community. |
| Outcome: | The proposed test collection comprehends English, Portuguese and German and provides comparative baselines and an analysis of the results. |
DECM: Evaluating Bilingual ASR Performance on a Code-switching/mixing Benchmark (2024.lrec-main)
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| Challenge: | Code-switched (CSW) speech is a linguistic phenomenon that occurs when spoken utterances switch languages between sentences. |
| Approach: | They propose to use a dataset to evaluate German-English CSW speech . they show that the dataset includes splits with varying degrees of CSW . |
| Outcome: | The proposed dataset includes spontaneous speech from diverse domains, enabling realistic CSW evaluation in German-English. |
Counting the Bugs in ChatGPT’s Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model (2023.emnlp-main)
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Leonie Weissweiler, Valentin Hofmann, Anjali Kantharuban, Anna Cai, Ritam Dutt, Amey Hengle, Anubha Kabra, Atharva Kulkarni, Abhishek Vijayakumar, Haofei Yu, Hinrich Schuetze, Kemal Oflazer, David Mortensen
| Challenge: | Existing studies on large language models (LLMs) ignore the remarkable ability of humans to generalize and focus only on English. |
| Approach: | They conduct the first rigorous analysis of the morphological capabilities of ChatGPT in four typologically varied languages. |
| Outcome: | The proposed model massively underperforms purpose-built systems, particularly in English. |
A Corpus for Automatic Readability Assessment and Text Simplification of German (2020.lrec-1)
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| Challenge: | Using monolingual-only data, we can automate readability assessment and text simplification of simplified language. |
| Approach: | They present a corpus for automatic readability assessment and automatic text simplification for German using parallel and monolingual data. |
| Outcome: | The proposed corpus is compiled from web sources and contains information on text structure, typography, font style, and images. |
GeNRe: A French Gender-Neutral Rewriting System Using Collective Nouns (2025.findings-acl)
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| Challenge: | Gender rewriting is an NLP task that uses gendered forms to mitigate gender biases. |
| Approach: | They propose a French gender-neutral rewriting system using collective nouns, which are gender-fixed in French. |
| Outcome: | The proposed system detects gendered forms and replaces them with neutral or opposite forms. |
A Framenet and Frame Annotator for German Social Media (2022.lrec-1)
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| Challenge: | In corpus linguistics, semantic annotation is a valuable addition to ordinary, morphosyntactic tagging, lemmatization and dependency relations. |
| Approach: | They propose a parsing- and annotation-oriented framenet for German with almost 15,000 frames . they propose valency, syntactic function and semantic noun class as input conditions for frame disambiguation . |
| Outcome: | The proposed resource is based on a Danish/German study on hate speech . it achieves an overall F-score for frame senses of 93.6% on twitter . |
Multilingual Simultaneous Neural Machine Translation (2021.findings-acl)
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| Challenge: | Simultaneous machine translation (SIMT) involves translating source utterances to the target language in real-time before the speaker utterrance completes. |
| Approach: | They propose a multilingual approach to simultaneous machine translation where a single model simultaneously translates between multiple languages. |
| Outcome: | The proposed multilingual approach improves on two Germanic and three Romance languages and is on-par or better than the universal model trained for all languages. |
FooTweets: A Bilingual Parallel Corpus of World Cup Tweets (L18-1)
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| Challenge: | a new study analyzes the nature of twitter data and compares it with other social networking websites. |
| Approach: | They develop a parallel corpus of tweets for an English-German pair that can be translated into German using a machine translation tool. |
| Outcome: | The proposed method can be used to translate tweets from English to German using a parallel corpus of 4, 000 tweets. |
GRhOOT: Ontology of Rhetorical Figures in German (2022.lrec-1)
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| Challenge: | GRhOOT is a domain ontology of rhetorical figures in the German language . the goal is to allow for easier detection of non-literal language based tasks . |
| Approach: | GRhOOT is a domain ontology of 110 rhetorical figures in the german language . the goal is to allow for easier detection and sentiment analysis . |
| Outcome: | The ontology of rhetorical figures in the German language is based on 110 rhetorical figure domains . the goal is to make the ontologies more accurate and to allow for easier detection . |
Building a Multilingual Taxonomy of Olfactory Terms with Timestamps (2022.lrec-1)
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| Challenge: | olfactory references play a crucial role in our memory and experiences . but only few works in NLP have attempted to capture this sensory dimension from a computational perspective. |
| Approach: | They describe a process that has led to the semi-automatic development of a taxonomy for olfactory information in four languages (English, French, German and Italian) |
| Outcome: | The proposed taxonomy can be extended using existing language models and n-grams to include olfactory terms in four languages. |
Multilingual Nonce Dependency Treebanks: Understanding how Language Models Represent and Process Syntactic Structure (2024.naacl-long)
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| Challenge: | a number of studies have focused on making explicit the linguistic information encoded in language models (LMs) however, this method has been criticized for various reasons. |
| Approach: | They introduce a framework for creating nonce treebanks for multilingual UD corpora . they investigate word co-occurrence statistics and show how nonce data affects the performance of syntactic dependency probes. |
| Outcome: | The proposed framework satisfies syntactic argument structure and ensures grammaticality via language-specific rules. |
Non-autoregressive Text Editing with Copy-aware Latent Alignments (2023.emnlp-main)
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| Challenge: | Seq2Edit approaches still face several challenges such as inflexibility in generation and difficulty in generalizing to other languages. |
| Approach: | They propose a non-autoregressive text editing method that models the edit process with latent CTC alignments and introduces the copy operation into the edit space. |
| Outcome: | The proposed method outperforms existing Seq2Edit models and achieves similar or even better results than Seq1Edit with over 4 speedup. |
DiscoGeM 2.0: A Parallel Corpus of English, German, French and Czech Implicit Discourse Relations (2024.lrec-main)
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| Challenge: | DiscoGeM 2.0 is a crowdsourced, parallel corpus of 12,834 implicit discourse relations . implicit discourse relationships are highly ambiguous and can have various interpretations . |
| Approach: | They propose a crowdsourced annotation method that can be extended to other languages . they propose to annotate 12,834 implicit discourse relations in German, German, French and Czech data . |
| Outcome: | The proposed method can be extended to other languages and reveals that implicit relations inferred in one language may differ from those inferted in the translation. |
A Neural Network Model for Part-Of-Speech Tagging of Social Media Texts (L18-1)
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| Challenge: | Recent approaches based on end-to-end Deep Neural Networks (DNNs) have shown promising results for Natural Language Processing (NLP). |
| Approach: | They propose a neural network model for part-of-speech (POS) tagging of User-Generated Content (UGC) such as Twitter, Facebook and Web forums that uses character and word representations. |
| Outcome: | The proposed model is end-to-end and uses character and word representations . it is compared with existing models on social media in English, german, french, italian and spanish . |
Building Bridges: A Dataset for Evaluating Gender-Fair Machine Translation into German (2024.findings-acl)
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| Challenge: | a societal movement towards using gender-fair language exists, but gender-free German is barely supported in machine translation. |
| Approach: | They propose to use a community-created gender-fair language dictionary to study gender-neutral German . they also use encyclopedic text and parliamentary speeches to translate the words in isolation . |
| Outcome: | The proposed study shows that most systems produce mainly masculine forms and rarely gender-neutral variants. |
A Study on Accessing Linguistic Information in Pre-Trained Language Models by Using Prompts (2023.emnlp-main)
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| Challenge: | Existing methods to access linguistic information in pre-trained multilingual language models are difficult to use. |
| Approach: | They propose prompting and formulate linguistic tasks to test the LM's access to explicit grammar principles and find out what type of information can be obtained . |
| Outcome: | The proposed method can provide access to linguistic features in pre-trained models, but some are harder to capture . |
Speech Foundation Models and Crowdsourcing for Efficient, High-Quality Data Collection (2025.coling-main)
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| Challenge: | Existing methods for crowdsourcing data collection require a human workforce, which is hard to sustain. |
| Approach: | They propose to use Speech Foundation Models to automate validation processes . they find that SFMs can reduce reliance on human validation . |
| Outcome: | The proposed model reduces the reliance on human validation without degrading the quality of the final data. |
An Empirical Study of Generation Order for Machine Translation (2020.emnlp-main)
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| Challenge: | a recent study of generation order for machine translation shows it does not affect output quality . Neural sequence models have been successfully applied to a broad range of tasks in recent years . |
| Approach: | They propose a soft order-reward framework that enables models to follow arbitrary oracle generation policies. |
| Outcome: | The proposed framework explores a wide variety of generation orders including uninformed orders, location-based orders, frequency-based or model-based orderings, and model-driven orders. |
Frustratingly Simple but Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic Parsing (2021.emnlp-main)
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| Challenge: | Existing training data is limited for languages other than English, so is the performance of the developed parsers. |
| Approach: | They propose to apply a pre-trained multilingual model to Italian, German and Dutch parsers where only a small number of manually annotated parses are available. |
| Outcome: | The proposed model improves on six parsers in English and Italian, German and Dutch, with the addition of universal dependency relations and universal POS tags as model-agnostic features. |
MultiMSD: A Corpus for Multilingual Medical Text Simplification from Online Medical References (2025.findings-acl)
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| Challenge: | Medical texts contain technical terms, and non-experts often cannot use information effectively. |
| Approach: | They propose a method for training medical text simplification models to actively paraphrase medical terms. |
| Outcome: | The proposed method improves the performance of medical text simplification in nine languages. |
Multilingual Clustering of Streaming News (D18-1)
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| Challenge: | a novel method for clustering news across languages is proposed . a key challenge in handling news streams is that they must be generated on the fly . |
| Approach: | They propose a method for clustering news across languages into monolingual and crosslingual clusters . they use real news datasets in multiple languages to find an ever growing number of cluster labels . |
| Outcome: | The proposed method produces state-of-the-art results on real news datasets in German, English and Spanish. |
Multi3Hate: Multimodal, Multilingual, and Multicultural Hate Speech Detection with Vision–Language Models (2025.naacl-long)
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| Challenge: | a new study shows that cultural background significantly affects multimodal hate speech moderation models . a limited dataset excludes multi-modal forms of hate and excludes non-English-speaking cultures . the lowest pairwise label agreement between the USA and India is due to cultural factors . |
| Approach: | They use a multimodal and multilingual parallel hate speech dataset to examine cultural differences . they find that cultural background significantly affects multimodal hate speech annotation . |
| Outcome: | The proposed dataset shows that cultural background significantly affects multimodal hate speech annotation. |
Cross-Linguistic Syntactic Evaluation of Word Prediction Models (2020.acl-main)
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| Challenge: | A range of studies have concluded that neural word prediction models can distinguish grammatical from ungrammatically sentences with high accuracy. |
| Approach: | They propose to use CLAMS to evaluate LSTM and multilingual BERT models. |
| Outcome: | The proposed model can learn syntax on English, French, German, Hebrew and Russian, and LSTM language models on multilingual and multilingual models. |
Systematic Analysis of Image Schemas in Natural Language through Explainable Multilingual Neural Language Processing (2022.coling-1)
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| Challenge: | Existing methods for automatic detection of image schemas in natural language rely on specific assumptions about word classes as indicators of spatio-temporal events. |
| Approach: | They propose to train a supervised classifier that classifies natural language expressions into image schemas using a large dataset of examples from image schema literature. |
| Outcome: | The proposed model performs best in German, Russian, and French, and is based on a small dataset of examples from image schema literature. |
Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition (L18-1)
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| Challenge: | a Swiss German speech recognizer is trained using a standard German annotation model. |
| Approach: | They propose to train a Swiss German speech recognition system using a standard German annotation model. |
| Outcome: | The proposed system is based on a standard German annotation model and a grapheme-to-phoneme conversion model. |
The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers (2022.lrec-1)
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Thomas Kolb, Sekanina Katharina, Bettina Manuela Johanna Kern, Julia Neidhardt, Tanja Wissik, Andreas Baumann
| Challenge: | Existing methods for sentiment analysis are limited in the Austrian German domain due to lexical idiosyncrasies and word sentiment. |
| Approach: | They propose to use crowd-sourced crowd-sourcing to create a sentiment dictionary for Austrian German . they use an austriacism list and a posting data set to increase the diversity of the language resource. |
| Outcome: | The proposed dictionary is available for future research and free to use for anyone. |
UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages (2020.lrec-1)
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| Challenge: | Sentiment lexica are vital for sentiment analysis in absence of document-level annotations . linguistic resources are limited for at least a few hundred languages, putting them at risk of extinction . |
| Approach: | They introduce UniSent universal sentiment lexica for 1000+ languages . they use a Bible corpus to project sentiment information from English to other languages based on Twitter data . |
| Outcome: | The proposed method mitigates domain mismatch between Bible and Twitter by using embeddings . it compares to other sentiment seeding methods in a subset of languages with ground truth available . |
Like a Baby: Visually Situated Neural Language Acquisition (P19-1)
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| Challenge: | A multi-modal neural architecture outperforms its equivalent trained on language alone with a 2% decrease in perplexity . |
| Approach: | They propose to use visual context to train neural language models to perform next-word prediction. |
| Outcome: | The proposed model outperforms its equivalent trained on language with 2% decrease in perplexity even when no visual context is available at test. |
Morphological Complexity of Children Narratives in Eight Languages (2022.lrec-1)
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Gordana Hržica, Chaya Liebeskind, Kristina Š. Despot, Olga Dontcheva-Navratilova, Laura Kamandulytė-Merfeldienė, Sara Košutar, Matea Kramarić, Giedrė Valūnaitė Oleškevičienė
| Challenge: | morphological complexity of a corpus representing the language production of younger and older children is compared across different languages. |
| Approach: | a study compares morphological complexity of a corpus representing language production of younger and older children across different languages. |
| Outcome: | The results show that younger children corpora have lower morphological complexity than older children corpus for Spanish and Russian. |
Swiss-AL: A Multilingual Swiss Web Corpus for Applied Linguistics (2020.lrec-1)
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| Challenge: | Swiss-AL is a multilingual web corpus for Applied Linguistics that supports data-based and data-driven research on societal and political discourses in Switzerland. |
| Approach: | They propose a multilingual Swiss web corpus for Applied Linguistics that supports data-based research on societal and political discourses in Switzerland. |
| Outcome: | The Swiss Web Corpus for Applied Linguistics (SWS) is a multilingual collection of texts from selected web sources. |
Auto-hMDS: Automatic Construction of a Large Heterogeneous Multilingual Multi-Document Summarization Corpus (L18-1)
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| Challenge: | Existing datasets for automatic text summarization are small and focused on newswires. |
| Approach: | They propose to automatically generate a large multilingual multi-document summarization corpus using Wikipedia articles as summaries and to automatically search for appropriate source documents. |
| Outcome: | The proposed corpus contains 7,316 topics in English and German with different summary lengths and number of source documents. |
Tracing Syntactic Change in the Scientific Genre: Two Universal Dependency-parsed Diachronic Corpora of Scientific English and German (2022.lrec-1)
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| Challenge: | a recent study has focused on the syntactic development of scientific discourse in English and German. |
| Approach: | They present two comparable diachronic corpora of scientific English and German from the Late Modern Period (17th c.–19th d.) annotated with Universal Dependencies. |
| Outcome: | The presented corpora are comparable to existing studies on grammatical change in English and German . the results show that the pre-processing steps significantly improve parsing accuracy . |
RRGparbank: A Parallel Role and Reference Grammar Treebank (2022.lrec-1)
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Tatiana Bladier, Kilian Evang, Valeria Generalova, Zahra Ghane, Laura Kallmeyer, Robin Möllemann, Natalia Moors, Rainer Osswald, Simon Petitjean
| Challenge: | Existing treebanks for Role and Reference Grammar (RRG) are not yet available. |
| Approach: | They propose to use a multilingual parallel treebank for Role and Reference Grammar to apply RRG to large-scale corpus annotations of 1984 and its translations. |
| Outcome: | The proposed treebank contains annotations of Orwell's 1984 and translations thereof. |
Unifying Morphology Resources with OntoLex-Morph. A Case Study in German (2022.lrec-1)
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| Challenge: | OntoLex is a widely used community standard for machine-readable lexical resources on the web. |
| Approach: | They propose a module for representing morphology that can be used to encode and integrate morphological resources on a unified basis. |
| Outcome: | The proposed module can be used to represent morphological resources on a unified basis. |
Common Voice: A Massively-Multilingual Speech Corpus (2020.lrec-1)
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Rosana Ardila, Megan Branson, Kelly Davis, Michael Kohler, Josh Meyer, Michael Henretty, Reuben Morais, Lindsay Saunders, Francis Tyers, Gregor Weber
| Challenge: | Common Voice is a massively-multilingual collection of transcribed speech intended for speech technology research and development. |
| Approach: | They propose to use Mozilla’s DeepSpeech Speech-to-Text toolkit to perform multilingual automatic speech recognition experiments. |
| Outcome: | The proposed corpus is the largest in the public domain for speech recognition, both in terms of hours and languages. |
Swap and Predict – Predicting the Semantic Changes in Words across Corpora by Context Swapping (2023.findings-emnlp)
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| Challenge: | Detecting semantic changes of words is an important task for various NLP applications that must make time-sensitive predictions. |
| Approach: | They propose a method that randomly swaps contexts between two different corpora to detect whether a given word changes its meaning . they then use a pretrained masked language model to generate contextualised word embeddings of w, which are then used to predict the semantic changes of words in four languages . |
| Outcome: | The proposed method achieves significant performance improvements compared to baselines for the English semantic change prediction task. |
Denoising Large-Scale Image Captioning from Alt-text Data Using Content Selection Models (2022.coling-1)
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| Challenge: | Recent approaches to training large-scale image captioning (IC) models often fall short in terms of performance in leveraging noisy datasets in favor of clean annotations. |
| Approach: | They propose a technique that breaks down the task into two smaller, more controllable tasks - skeleton prediction and skelet-based caption generation. |
| Outcome: | The proposed method can generate better and denoised captions when using noisy datasets. |
LemmaTag: Jointly Tagging and Lemmatizing for Morphologically Rich Languages with BRNNs (D18-1)
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| Challenge: | We compare morphologically rich languages with analytical languages like English due to the large vocabulary size and data sparsity. |
| Approach: | They propose a featureless neural network architecture that generates part-of-speech tags and lemmas for sentences by using bidirectional RNNs with character-level and word-level embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art models in Czech, German, and Arabic. |
All That Glitters is Not Gold: A Gold Standard of Adjective-Noun Collocations for German (2020.lrec-1)
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| Challenge: | Using the GerCo dataset, we identify adjective-noun collocations in German and compare them with statistical associations measures. |
| Approach: | They present a GerCo dataset of adjective-noun collocations for German, such as alter Freund ‘old friend’ and tiefe Liebe ‘deep love’. |
| Outcome: | The GerCo dataset contains 4,732 positive and negative instances of collocations and covers all 16 semantic classes of adjectives defined in the German wordnet GermaNet. |
From Shijing to English and German: Resources and Evaluation for LLM Translation of Early Chinese Poetry (2026.findings-acl)
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| Challenge: | Large language models (LLMs) show promise in literary translation, but their performance in poetry remains unexplored. |
| Approach: | They propose a framework that integrates knowledge-driven, rule-based, and LLM-as-judge metrics into a Shijing corpus . their code, lexical KB, and corpus reconstruction protocols are available at https://github.com/ML-KULeuven/ShijingLLMTrans. |
| Outcome: | The proposed framework achieves higher human correlation than traditional metrics and high statistical stability. |
Continual Learning in Multilingual Sign Language Translation (2025.naacl-long)
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| Challenge: | Despite the low translation quality of sign language, many machine learning approaches are still in its infancy. |
| Approach: | They propose to use continual learning for mul- tilingual SLT to improve translation quality. |
| Outcome: | The proposed methods outperform baseline and fine-tuning approaches in sign language translation. |
Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models (2022.acl-long)
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| Challenge: | Existing methods for SRL identify semantic dependencies that specify the semantic role of arguments in relation to predicates. |
| Approach: | They propose a mixture model-based end-to-end method to model syntactic-semantic dependency correlation in Semantic Role Labeling. |
| Outcome: | The proposed method improves performance in English, German, and Spanish . it achieves small but statistically significant improvement over baseline methods . |
taz2024full: Analysing German Newspapers for Gender Bias and Discrimination across Decades (2025.findings-acl)
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| Challenge: | a large corpus of German newspaper articles is available for free in other languages, such as English. |
| Approach: | They propose to use taz2024full to analyse gender representation across four decades of reporting. |
| Outcome: | The proposed corpus supports a wide range of applications from diachronic language analysis to critical media studies. |
Building Named Entity Recognition Taggers via Parallel Corpora (L18-1)
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| Challenge: | Existing methods to generate semantic processors for languages lacking hand curated data are inefficiently slow and unaffordable in terms of human resources and economic costs. |
| Approach: | They propose to use statistical word alignments to project annotations from multiple sources to a target language. |
| Outcome: | The proposed method is effective to transport NER annotations across languages . it can generate a good statistical model for a new target language . |
AGB-DE: A Corpus for the Automated Legal Assessment of Clauses in German Consumer Contracts (2024.acl-long)
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| Challenge: | Annotated contracts are laborious task performed by companies, law firms, NGOs and the scientific community. |
| Approach: | They present a corpus of 3,764 clauses from German consumer contracts annotated by legal experts with a clause in the contract. |
| Outcome: | The proposed framework outperforms openly available models in detecting potentially void clauses. |
A Corpus for Multilingual Document Classification in Eight Languages (L18-1)
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| Challenge: | a subset of the Reuters corpus volume 2 is used to evaluate cross-lingual document classification . current best practice is to evaluate document classification on resources in one language and transfer it to another without additional resources. |
| Approach: | They propose to use a subset of the Reuters corpus to evaluate cross-lingual document classification . they propose to add Italian, Russian, Japanese and Chinese to the subset . |
| Outcome: | The proposed subset of the Reuters corpus has balanced class priors for eight languages. |
Multi-word Measures: Modeling Semantic Change in Compound Nouns (2025.findings-acl)
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| Challenge: | Compound words provide a multifaceted challenge for diachronic models of semantic change . novel sense-targeting approach targets both noun compounds and their constituent parts . |
| Approach: | They propose a dataset of relatedness judgements of noun compounds in English and german . they use contrasting vector representations to evaluate their ability to cluster example sentence pairs . |
| Outcome: | The proposed approach captures diachronic meaning changes for multi-word expressions without condensing individual senses into an aggregate value. |
Incorporating Contextual Information for Language-Independent, Dynamic Disambiguation Tasks (L18-1)
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| Challenge: | a proposed multimodal system can resolve syntactic ambiguities by exploiting external evidence, says a researcher . a parser that processes linguistic information is expected to handle syntakically unambiguous sentences, but it cannot. |
| Approach: | They propose to exploit external contextual information to resolve ambiguous sentences . they propose to use data-driven and grammar-based approaches to solve ambiguities . |
| Outcome: | The proposed system confirms this hypothesis in experiments on syntactically ambiguous sentences. |
I Wish I Would Have Loved This One, But I Didn’t – A Multilingual Dataset for Counterfactual Detection in Product Review (2021.emnlp-main)
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| Challenge: | Using machine translation, counterfactual statements are often found in natural languages. |
| Approach: | They annotate a multilingual CFD dataset from Amazon product reviews covering counterfactuals written in English, German, and Japanese languages. |
| Outcome: | The proposed dataset is robust against selection biases due to cue phrase-based sentence selection. |
MucLex: A German Lexicon for Surface Realisation (2020.lrec-1)
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| Challenge: | lexica for rule-based surface realisers for languages other than English are often scarce. |
| Approach: | They propose a German lexicon for the Natural Language Generation task of surface realisation based on the crowd-sourced online lexicont Wiktionary. |
| Outcome: | The proposed lexicon contains more than 100,000 lemmata and 670,000 different word forms. |
ValNorm Quantifies Semantics to Reveal Consistent Valence Biases Across Languages and Over Centuries (2021.emnlp-main)
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| Challenge: | Word embeddings learn implicit biases from word co-occurrence statistics . valNorm is a new intrinsic evaluation task and method to quantify affect in word embedded word sets . |
| Approach: | They propose a method to quantify valence dimension of affect in human-rated word sets . they apply ValNorm to embeddings from seven languages and 200 years of text . |
| Outcome: | The proposed method achieves a high accuracy in quantifying the valence of non-discriminatory, non-social group word sets. |
Evaluating Webcam-based Gaze Data as an Alternative for Human Rationale Annotations (2024.lrec-main)
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| Challenge: | We compare webcam-based eye-tracking recordings with human-annotated rationales to evaluate importance scores. |
| Approach: | They compare webcam-based eye-tracking recordings with attention-based importance scores for 4 different multilingual Transformer-based language models. |
| Outcome: | The proposed method is comparable to human rationales in linguistic analysis. |
Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset (2022.acl-long)
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| Challenge: | Automated short answer grading systems lack content-focused elaborated feedback datasets. |
| Approach: | They propose to use the Short Answer Feedback dataset to encourage research on explainable and understandable feedback systems. |
| Outcome: | The proposed model can be used to grade answers and explain where and why mistakes were made. |
Recovering document annotations for sentence-level bitext (2024.findings-acl)
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| Challenge: | In machine translation, historical models were incapable of handling longer contexts, so the lack of document-level datasets was less noticeable. |
| Approach: | They propose a document-level filtering technique that discards document- level metadata. |
| Outcome: | The proposed method improves translation without degradation of sentence-level translation. |
Multilingual Neural RST Discourse Parsing (2020.coling-main)
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| Challenge: | Existing studies on text discourse parsing for English are limited due to the lack of annotated data. |
| Approach: | They propose to use multilingual vector representations and segment-level translation to establish a neural, cross-lingual discourse parser. |
| Outcome: | The proposed model achieves state-of-the-art on cross-lingual, document-level discourse parsing on all sub-tasks. |
The Lou Dataset - Exploring the Impact of Gender-Fair Language in German Text Classification (2024.emnlp-main)
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| Challenge: | Gender-fair language fosters inclusion by addressing all genders or using neutral forms. |
| Approach: | They present a dataset that provides high-quality reformulations for German text classification . they find substantial label flips, reduced prediction certainty, and altered attention patterns . |
| Outcome: | The proposed dataset provides high-quality reformulations for German text classification . it finds label flips, reduced prediction certainty, and significantly altered attention patterns . |
Predicting Declension Class from Form and Meaning (2020.acl-main)
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| Challenge: | phonological form and meaning of nouns can provide imperfect clues, but they can also be indicative of grammatical gender. |
| Approach: | They propose a method to measure how much information can be gleamed from knowing the form and/or meaning of nouns. |
| Outcome: | The proposed method provides additional quantitative support for a classic linguistic finding that form and meaning are relevant for the classification of nouns into declensions. |
You need to MIMIC to get FAME: Solving Meeting Transcript Scarcity with Multi-Agent Conversations (2025.findings-acl)
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| Challenge: | Existing tools for meeting summarization are limited due to privacy and expensive manual annotation. |
| Approach: | They propose a meeting synthesis framework that generates meeting transcripts on a given knowledge source by defining psychologically grounded participant profiles, outlining the conversation, and orchestrating a large language model (LLM) debate. |
| Outcome: | The proposed framework generates meeting transcripts on a given knowledge source by defining psychologically grounded participant profiles, outlining the conversation, and orchestrating a large language model debate. |
Modeling Persuasive Discourse to Adaptively Support Students’ Argumentative Writing (2022.acl-long)
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| Challenge: | Argumentation is an omnipresent rudiment of daily communication and thinking . humans struggle to develop argumentation skills due to a lack of individual and instant feedback in their learning process. |
| Approach: | They propose an argumentation annotation approach to model argumentative discourse in student-written business model pitches and embed it into an adaptive writing support system for students that provides individual argumentation feedback. |
| Outcome: | The proposed method annotates a corpus of 200 business model pitches in german and measures their self-efficacy and ease-of-use in a real-world writing exercise. |
MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation (2025.emnlp-main)
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Khai Le-Duc, Tuyen Tran, Bach Phan Tat, Nguyen Kim Hai Bui, Quan Dang Anh, Hung-Phong Tran, Thanh Thuy Nguyen, Ly Nguyen, Tuan Minh Phan, Thi Thu Phuong Tran, Chris Ngo, Khanh Xuan Nguyen, Thanh Nguyen-Tang
| Challenge: | Multilingual speech translation (ST) and machine translation (MT) in the medical domain enhances patient care by enabling efficient communication across language barriers. |
| Approach: | They present a large-scale ST dataset for the medical domain spanning all translation directions in Vietnamese, English, German, French, and Simplified/Traditional Chinese, together with the models. |
| Outcome: | The multi-language speech translation (ST) and machine translation (MT) in the medical domain is the largest medical MT dataset and the largest many-to-many multilingual ST among all domains. |
A Multilingual Dataset for Evaluating Parallel Sentence Extraction from Comparable Corpora (L18-1)
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| Challenge: | BUCC Shared Task aims to extract parallel sentences from comparable corporad . resulting corpus contains about 3.5 million distinct sentences in english, french, german, Russian, and Chinese . |
| Approach: | They present challenges faced to build a parallel sentences dataset from comparable corporad . they emphasize issues faced to include Chinese as one of the languages . |
| Outcome: | The 2017 BUCC Shared Task was a first for this task . the dataset contains 3.5 million sentences in English, French, German, Russian, and Chinese . |
Acquiescence Bias in Large Language Models (2025.findings-emnlp)
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| Challenge: | Acquiescence bias is a tendency of humans to agree with statements in surveys, independent of their actual beliefs. |
| Approach: | They investigate the presence of acquiescence bias in Large Language Models across different models, tasks, and languages. |
| Outcome: | The proposed model shows that LLMs display a bias towards answering no regardless of whether it indicates agreement or disagreement. |
Evaluating Inflectional Complexity Crosslinguistically: a Processing Perspective (L18-1)
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| Challenge: | a cognitively motivated method for evaluating the inflectional complexity of a language is proposed . authors argue that some languages are inflectionally more complex than others . |
| Approach: | They propose a cognitively motivated method for evaluating inflectional complexity of a language . they use a recurrent self-organising neural network to learn "raw" inflected word forms . |
| Outcome: | The proposed method is independent of meta-linguistic issues and language-specific typological aspects. |
Building a Morphological Treebank for German from a Linguistic Database (L18-1)
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| Challenge: | German is a language with complex morphological processes. |
| Approach: | They propose a morphological treebank for German based on a German morphology database and a Perl script for the generation. |
| Outcome: | The proposed treebank is based on the German lexical database CELEX and is able to generate 40,000 morphological trees with a grade of detail that can be chosen according to the requirements of the applications. |
Come hither or go away? Recognising pre-electoral coalition signals in the news (2021.emnlp-main)
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Ines Rehbein, Simone Paolo Ponzetto, Anna Adendorf, Oke Bahnsen, Lukas Stoetzer, Heiner Stuckenschmidt
| Challenge: | In this paper, we decompose the task of recognizing from the news coverage leading up to an election the (un)willingness of political parties to form a coalition into two related, but distinct tasks. |
| Approach: | They propose a task of recognizing from news coverage the (un)willingness of political parties to form a coalition from text and a sub-task of predicting the polarity of the signal. |
| Outcome: | The proposed approach improves over a strong monolingual transfer learning baseline. |
Towards Multilingual spoken Visual Question Answering system using Cross-Attention (2025.coling-main)
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| Challenge: | Visual question answering (VQA) is a multi-modal translation challenge that requires the analysis of both images and questions simultaneously to generate appropriate responses. |
| Approach: | They propose a textless multilingual visual question answering dataset that incorporates speech-based questions in English, german, spanish and french. |
| Outcome: | The proposed framework is superior to existing frameworks for speech-based VQA . the proposed framework can generate better results for image, text and audio representations . |
Abstract Text Summarization: A Low Resource Challenge (D19-1)
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| Challenge: | Existing datasets for multilingual text summarization are difficult to construct and lack of human knowledge and language processing abilities in computers makes text summaries a challenging task. |
| Approach: | They propose an iterative data augmentation approach which uses synthetic data along with the real summarization data for the German language. |
| Outcome: | The proposed system improves on the development and test sets on the German language text using the state-of-the-art “Transformer” model. |
SemR-11: A Multi-Lingual Gold-Standard for Semantic Similarity and Relatedness for Eleven Languages (L18-1)
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| Challenge: | SemR-11 is a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages. |
| Approach: | This paper describes a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages. |
| Outcome: | The dataset is a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages. |
Parsing as Tagging (2020.lrec-1)
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| Challenge: | Existing methods for dependency parsing treat parse as tagging, but they are not perfect. |
| Approach: | They propose a simple yet accurate method that treats parsing as tagging . they use a sequence model with a bidirectional LSTM over BERT embeddings . |
| Outcome: | The proposed method outperforms the state-of-the-art method on universal dependency (UD) by 1.76% unlabeled attachment score (UAS) for English, 1.98% UAS for French, and 1.16% UAS in German. |
The EDGeS Diachronic Bible Corpus (2020.lrec-1)
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| Challenge: | EDGeS is a diachronic and parallel corpus of Bible translations in Dutch, English, German and Swedish . it is intended to be used for longitudinal studies of complex verb constructions in Germanic . |
| Approach: | They present the EDGeS Diachronic Bible Corpus, a diachronic corpus of Bible translations in Dutch, English, German and Swedish . they use a synchronically and synchronly parallel corpus to study complex verb constructions in Germanic . |
| Outcome: | The EDGeS is a diachronic and parallel corpus of Bible translations in Dutch, English, German and Swedish spanning six and a half centuries. |
MLSUM: The Multilingual Summarization Corpus (2020.emnlp-main)
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| Challenge: | Existing biases in multi-lingual datasets are limiting the use of multilingual data in document summarization tasks. |
| Approach: | They present MLSUM, the first large-scale MultiLingual SUMmarization dataset. |
| Outcome: | The proposed dataset contains 1.5M+ article/summary pairs in five different languages. |
MLQA: Evaluating Cross-lingual Extractive Question Answering (2020.acl-main)
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| Challenge: | Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. |
| Approach: | They present a multi-way aligned extractive QA evaluation benchmark in 7 languages . they evaluate state-of-the-art cross-lingual models and machine-translation-based baselines . |
| Outcome: | The proposed model is based on MLQA, which has over 12K instances in english and 5K in each other language. |
Pronunciation Dictionaries for the Alsatian Dialects to Analyze Spelling and Phonetic Variation (L18-1)
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| Challenge: | a new study compares phonetic transcriptions of Alsatian, German and French with existing pronunciation dictionaries . Alsatic dialects do not have a standardized spelling system, despite literary history dating back to the 19th century . |
| Approach: | They propose new pronunciation dictionaries for the under-resourced Alsatian dialects . they compare them with existing phonetic transcriptions of Alsalian, German and French . |
| Outcome: | The proposed dictionaries are compared with existing phonetic transcriptions of Alsatian, German and French to examine the relationship between speech and writing. |
Code-switched inspired losses for spoken dialog representations (2021.emnlp-main)
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| Challenge: | We introduce new pretraining losses tailored to learn generic multilingual spoken dialogue representations . goal is to expose model to code-switched language . |
| Approach: | They propose to build a pretraining corpus of multilingual conversations in five different languages from OpenSubtitles. |
| Outcome: | The proposed models perform better in monolingual and multilingual settings. |
SLABERT Talk Pretty One Day: Modeling Second Language Acquisition with BERT (2023.acl-long)
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| Challenge: | NLP literature has not given enough attention to the phenomenon of negative transfer . positive transfer refers to the facilitating effects of one language in acquiring another and negative transfer refer to the negative effects between the learner's native [L1] and target [L2] languages. |
| Approach: | They build a Mutlilingual Age Ordered CHILDES dataset to understand the degree to which native Child-Directed Speech (CDS) can help or conflict with English language acquisition. |
| Outcome: | The proposed model enables us to understand the degree to which native Child-Directed Speech (CDS) can help or conflict with English language acquisition. |
MultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning (2023.acl-long)
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| Challenge: | Existing methods for supervised visual captioning require large scale of images or videos paired with descriptions in a specific language. |
| Approach: | They propose a zero-shot approach that generates captions for different scenarios without labeling . they use concept prompts to retrieve concepts and auto-encode them to learn writing styles . |
| Outcome: | The proposed approach generates captions for different scenarios and languages without labeled vision-caption pairs. |
Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistance (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are increasingly utilized in educational tasks such as providing writing suggestions to students. |
| Approach: | They conduct a large-scale user study with 231 students writing business case peer reviews in german. |
| Outcome: | The proposed model does not carry bias in the feedback loops of the students . |
REPROLANG 2020: Automatic Proficiency Scoring of Czech, English, German, Italian, and Spanish Learner Essays (2020.lrec-1)
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| Challenge: | a new paper aims to reproduce the work described in Vajjala & Rama (2018) . the paper focuses on features-based and neural approaches to essay scoring in Czech, German and Italian . |
| Approach: | They propose to replicate the work described in Vajjala & Rama 2018, ‘Experiments with universal CEFR classification’, as part of REPROLANG 2020. |
| Outcome: | The proposed methods perform better than feature-based models for large text datasets, though neural network modifications do bring performance closer to the best feature-driven models. |
GPT-3.5 for Grammatical Error Correction (2024.lrec-main)
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| Challenge: | Recent work shows that GPT-3.5 struggles with several error types, including punctuation mistakes, tense errors, syntactic dependencies between words, and lexical compatibility at the sentence level. |
| Approach: | They evaluate GPT-3.5 for grammatical error correction in multiple languages . they use it to re-rank correction hypotheses generated by other GEC models . |
| Outcome: | The proposed model performs well in English and Russian, but struggles with errors in other languages. |
Defying Wikidata: Validation of Terminological Relations in the Web of Data (2020.lrec-1)
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| Challenge: | a new approach to validate terminological data retrieved from open encyclopaedic knowledge bases is needed . the legal domain is one of the most valuable areas of knowledge in the world . |
| Approach: | They propose to validate terminological data retrieved from open encyclopaedic knowledge bases by enriching them with information from existing resources in the Semantic Web. |
| Outcome: | The proposed method validates terms from open encyclopaedic knowledge bases in four languages. |
Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Language (2024.lrec-main)
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| Challenge: | Existing methods to extract relationships are limited to English and require annotating datasets in order to be expensive and time-consuming. |
| Approach: | They apply guided distant supervision to create a large biographical relationship extraction dataset for German using 80,000 instances for nine relationship types. |
| Outcome: | The proposed dataset is the largest biographical German relationship extraction dataset. |
HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking (2024.lrec-main)
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| Challenge: | determining the trustworthiness of online medical content is challenging in the digital age . fact-checking is an approach to assess the veracity of factual claims . a new dataset is presented to help advance automated fact- checking . |
| Approach: | They propose a dataset that assesses the veracity of factual claims using evidence from credible sources. |
| Outcome: | The proposed dataset can be used for automated fact-checking tasks. |
Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains (2020.lrec-1)
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| Challenge: | Word Sense Disambiguation (WSD) is a field of NLP where data is usually tied to a specific language. |
| Approach: | They propose to release five large datasets annotated with word-senses in five different languages and 5 datasets in English for a different semantic domain. |
| Outcome: | The study shows that supervised models trained on the data achieve higher performance than those trained on other corpora. |
To Err Is Human, but Llamas Can Learn It Too (2024.findings-emnlp)
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| Challenge: | Specifically, we fine-tune Llama 2 LMs for error generation and find that this approach yields synthetic errors akin to human errors. |
| Approach: | They propose to fine-tune Llama 2 LMs for error generation and train GEC Llma models using these artificial errors. |
| Outcome: | The proposed approach outperforms state-of-the-art models with gains ranging between 0.8 and 6 F0.5 points across all languages tested. |
Models and Datasets for Cross-Lingual Summarisation (2021.emnlp-main)
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| Challenge: | Recent years have witnessed increased interest in abstractive summarisation thanks to the popularity of neural network models and the availability of datasets containing hundreds of thousands of document-summary pairs. |
| Approach: | They propose to create a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in . target language. |
| Outcome: | The proposed task can be applied to several other languages and covers twelve languages and directions. |
Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing methods to improve beam search quality are inadequate in many ways . a new approximation to the beam search curse has been proposed . |
| Approach: | They propose an approximation to minimum Bayes risk decoding that would solve the beam search curse. |
| Outcome: | The proposed approximation has no equivalent to the beam search curse. |
Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper (2023.findings-emnlp)
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| Challenge: | Several platforms currently rate news outlets according to their political bias, but they are not ready for use in the near future. |
| Approach: | They use ratings of authentic news outlets to create a multilingual corpus of news with coarse stance annotations along with automatically extracted topic annotations. |
| Outcome: | The proposed model can identify the editorial line of most unseen newspapers in English, German, Spanish and Catalan. |
Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling (2020.lrec-1)
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Dmitrii Aksenov, Julian Moreno-Schneider, Peter Bourgonje, Robert Schwarzenberg, Leonhard Hennig, Georg Rehm
| Challenge: | Abstractive summarization is an NLP task with many real-world applications. |
| Approach: | They propose to use a pre-trained language model to train a Transformer-based neural model . they propose a new method of BERT-windowing to allow chunk-wise processing of texts longer than the BERT window size . |
| Outcome: | The proposed model outperforms baseline models on CNN/Daily Mail dataset and shows its superiority on German dataset. |
DISCO: A Large Scale Human Annotated Corpus for Disfluency Correction in Indo-European Languages (2023.findings-emnlp)
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| Challenge: | Existing research on disfluency correction has primarily focused on English due to the unavailability of large-scale open-source datasets. |
| Approach: | They propose to use an annotated human-annotated corpus to analyze disfluency correction in four important Indo-European languages to demonstrate the benefits. |
| Outcome: | The proposed model improves BLEU scores by 5.65 points when used with a state-of-the-art machine translation system. |
Outbound Translation User Interface Ptakopět: A Pilot Study (2020.lrec-1)
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| Challenge: | a task called outbound translation is not uncommon for Internet users to have to produce a text in a foreign language they have very little knowledge of and are unable to verify the translation quality. |
| Approach: | They propose an open-source modular system to inspect human interaction with machine translation systems enhanced with additional subsystems such as backward translation and quality estimation. |
| Outcome: | The proposed system is able to produce a text in a foreign language with minimal knowledge and is compared with MT systems of mid-range quality. |
People and Places of Historical Europe: Bootstrapping Annotation Pipeline and a New Corpus of Named Entities in Late Medieval Texts (2023.findings-acl)
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| Challenge: | Pre-trained named entity recognition models are inaccurate on modern corpora due to differences in language OCR errors. |
| Approach: | They develop a named entity recognition (NER) corpus of 3.6M sentences from medieval charters written mainly in Czech, Latin, and German. |
| Outcome: | The proposed model achieves entity-level Precision of 72.81–93.98% with 58.14–81.77% Recall on a manually-annotated test dataset. |
Are Large Language Models Consistent over Value-laden Questions? (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) appear to bias survey answers toward certain values . however, some argue that LLMs are inconsistent to simulate particular values - a recent study . |
| Approach: | They define value consistency as similarity of answers across paraphrases, related questions and multilingual translations of a question to English, Chinese, German, and Japanese. |
| Outcome: | The proposed model is consistent across paraphrases, use-cases, translations, and within a topic. |
DEplain: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification (2023.acl-long)
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| Challenge: | Current text simplification research mostly focuses on English and on sentencelevel simplification. |
| Approach: | They propose to use a dataset of parallel, professionally written and manually aligned simplifications in plain German "plain DE" and "Einfache Sprache" they build a web harvester and experiment with automatic alignment methods to facilitate integration of non-aligned and to be-published parallel documents. |
| Outcome: | The proposed dataset of parallel, professionally written and manually aligned simplifications in plain German is extended to 750 document pairs and 3.5k sentence pairs. |
To Ask LLMs about English Grammaticality, Prompt Them in a Different Language (2024.findings-emnlp)
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| Challenge: | a study focuses on questions about grammar and fluency in multilingual LLMs . english is the dominant training language for all three models, but prompting in a different language often yields better results. |
| Approach: | They ask three multilingual language models in multiple languages to test their model's grammatical accuracy. |
| Outcome: | The language of the prompt can significantly affect model performance, the study finds . english is the dominant training language for all three models, the researchers show . |
Do LLMs learn a true syntactic universal? (2024.emnlp-main)
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| Challenge: | linguistics literature has debated whether large multilingual language models learn language universals . Typological generalizations are a key battleground in such debates - e.g. van der Hulst, 2023, chapter 7). |
| Approach: | They consider a candidate universal for language universals, the Final-over-Final Condition . they suggest that modern language models may need additional sources of bias to become truly human-like . |
| Outcome: | The proposed model only seems to recognize the Final-over-Final Condition in German, Russian, Hungarian and Serbian . |
MaiBaam: A Multi-Dialectal Bavarian Universal Dependency Treebank (2024.lrec-main)
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| Challenge: | Despite the success of the Universal Dependencies (UD) project, there is still a lack of diversity within high-resource languages and their closely related non-standard languages and dialects. |
| Approach: | They propose to annotate Bavarian with part-of-speech and syntactic dependency information manually in UD and to highlight morphosyntactical differences between the closely related languages. |
| Outcome: | The proposed treebank covers multiple genres including wiki, fiction, grammar examples, social, non-fiction and Bavarian. |
Large Language Models as Neurolinguistic Subjects: Discrepancy between Performance and Competence (2025.findings-acl)
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| Challenge: | Existing evaluations of Large Language Models (LLMs) reflect statistical rules that may not accurately represent LLMs’ true linguistic competence. |
| Approach: | They propose a method that combines minimal pair and diagnostic probing to analyze activation patterns across model layers. |
| Outcome: | The proposed method combines minimal pair and diagnostic probing to analyze activation patterns across model layers. |
Mitigating Catastrophic Forgetting in Language Transfer via Model Merging (2024.findings-emnlp)
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| Challenge: | Large language models have shown remarkable capabilities, particularly in English, but for less prevalent languages, performance can be significantly lower, making additional adaptation paramount. |
| Approach: | They propose a new adaptation method based on iteratively merging multiple models fine-tuned on a subset of available training data that reduces forgetting while maintaining learning on the target domain. |
| Outcome: | The proposed method outperforms LLAMA-3-8B-based models in German and German while maintaining learning on the target domain. |
Model-Agnostic Cross-Lingual Training for Discourse Representation Structure Parsing (2024.lrec-main)
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| Challenge: | Discourse Representation Structure (DRS) parsers are constrained when trained exclusively on monolingual data. |
| Approach: | They propose a cross-lingual training strategy that leverages cross-linguistic training data to train models in multiple languages. |
| Outcome: | The proposed method improves clause and graph parsing in English, German, Italian and Dutch. |
S2ST-Omni: Hierarchical Language-Aware SpeechLLM Adaptation for Multilingual Speech-to-Speech Translation (2026.findings-acl)
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| Challenge: | S2ST-Omni integrates a speech-to-text frontend with a modular, plug-and-play text-tospeech backend. |
| Approach: | They propose a compositional S2ST framework that integrates a speech-to-text frontend with a modular, plug-and-play text-tospeech backend. |
| Outcome: | The proposed framework outperforms existing frameworks in translation and synthesis . it integrates a speech-to-text translation frontend with a plug-and-play text-tospeech backend . |
NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition (2024.emnlp-main)
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| Challenge: | Existing approaches to named entity recognition often contain a significant percentage of incorrect labels for entity types and boundary boundaries. |
| Approach: | They propose a noise-robust learning approach that learns from data with partially incorrect labels. |
| Outcome: | The proposed methods are based on simulated noise and are easier to handle than simulated real noise caused by human error or semi-automatic annotation. |
GuardBench: A Large-Scale Benchmark for Guardrail Models (2024.emnlp-main)
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| Challenge: | Lack of a standard benchmark for guardrail models poses significant evaluation issues . lack of standardized benchmark makes it hard to compare results across scientific publications. |
| Approach: | They propose a large-scale benchmark for guardrail models comprising 40 safety evaluation datasets. |
| Outcome: | The proposed model achieves competitive results without specific fine-tuning without the need for specific fine tuning. |
The Linearity of the Effect of Surprisal on Reading Times across Languages (2023.findings-emnlp)
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| Challenge: | a large amount of insight into human language processing can be gleaned by studying word-by-word processing difficulty. |
| Approach: | They extend the study by examining eyetracking corpora of seven languages . they find evidence for superlinearity in some languages, but highly sensitive to language models . |
| Outcome: | The study extends existing studies on english to Danish, Dutch, English, German, Japanese, Mandarin, and Russian. |
PaReNT (Parent Retrieval Neural Tool): A Deep Dive into Word Formation across Languages (2024.lrec-main)
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| Challenge: | a significant portion of words in a language share one or more roots with other existing lexemes as a result of word-formation processes. |
| Approach: | They present a deep-learning-based multilingual tool that performs retrieval and word formation classification in seven languages. |
| Outcome: | The proposed tool performs retrieval and word formation classification in English, German, Dutch, Spanish, French, Russian, and Czech. |
Parsing Headed Constituencies (2024.lrec-main)
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| Challenge: | Using constituency and dependency trees, syntactic representations are preferred for tasks such as nominal phrase extraction and identification of terminology. |
| Approach: | They propose a parsing technique that generates headed constituency trees which combine information typically contained in constituency and dependency trees. |
| Outcome: | The proposed method generates headed constituency trees with discontinuities and can generate constituency tree with discontinuity. |
Debiasing Multilingual LLMs in Cross-lingual Latent Space (2025.emnlp-main)
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| Challenge: | Existing studies have evaluated their cross-lingual transferability by directly applying these methods to LLM representations, revealing their limited effectiveness across languages. |
| Approach: | They propose to perform debiasing in a joint latent space rather than directly on LLM representations by using an autoencoder trained on parallel TED talk scripts. |
| Outcome: | The proposed method improves both the overall debiasing performance and cross-lingual transferability of the proposed techniques across four languages. |
Resolving Legalese: A Multilingual Exploration of Negation Scope Resolution in Legal Documents (2024.lrec-main)
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| Challenge: | Negation scope resolution is a challenging task for NLP because of the complexity of legal texts and lack of annotated in-domain negation corpora. |
| Approach: | They propose to use annotated court decisions to improve negation scope resolution . they release annotations in german, french, and italian to train models without legal data . |
| Outcome: | The proposed models achieve token-level F1-scores of up to 86.7% in zero-shot and multilingual settings. |
Assessing the Role of Data Quality in Training Bilingual Language Models (2025.findings-emnlp)
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| Challenge: | a recent study shows that adding more languages can degrade performance for some languages while improving others. |
| Approach: | They propose a data filtering strategy to select high-quality bilingual training data with only high quality English data. |
| Outcome: | The proposed approach improves bilingual model performance by 2–4% and reduces bilingual models performance gaps to 1%. |
Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data (2024.lrec-main)
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Siyao Peng, Zihang Sun, Huangyan Shan, Marie Kolm, Verena Blaschke, Ekaterina Artemova, Barbara Plank
| Challenge: | Named Entity Recognition (NER) is a fundamental task to extract key information from texts, but annotated resources are scarce for dialects. |
| Approach: | They present the first dialectal NER dataset for German, BarNER, with 161K tokens annotated on Bavarian Wikipedia articles and tweets. |
| Outcome: | The proposed dataset improves on bar-wiki and moderately on bartweet with training first on Bavarian . |
MultiTEND: A Multilingual Benchmark for Natural Language to NoSQL Query Translation (2025.findings-acl)
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| Challenge: | Recent advances in NoSQL database support focus on English . however, the intricacy and heterogeneity of NoSqL query languages present a formidable challenge . |
| Approach: | They propose a multilingual benchmark for natural language to NoSQL query generation that covers six languages. |
| Outcome: | The proposed framework improves performance in English and non-English settings, while ignoring lexical and syntactic differences. |
Soft Well-Formed Semantic Parsing with Score-Based Selection (2024.lrec-main)
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| Challenge: | Semantic parsing is the task of translating natural language into a structured, formal semantic representation that can be interpreted by machines. |
| Approach: | They propose a score-based method to select well-formed outputs from candidates generated by beam search algorithms. |
| Outcome: | The proposed method reduces the number of ill-formed outputs and improves F1 scores in English. |
Multilingual Definition Modeling (2025.findings-acl)
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| Challenge: | Existing definition modeling tasks are mainly encoder-decoder-based, with no explicit definitions. |
| Approach: | They propose a multilingual study on definition modeling using monolingual dictionary data for four new languages. |
| Outcome: | The proposed task is based on monolingual dictionary data for four new languages . results show that multilingual models can perform on-pair with English but cannot leverage potential cross-lingual synergies . |
SwissSLi: The Multi-parallel Sign Language Corpus for Switzerland (2024.lrec-main)
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| Challenge: | Using a CC BY-NC-SA 4.0 license, this corpus contains parallel sign language videos and spoken language subtitles. |
| Approach: | They introduce SwissSLi, the first sign language corpus that contains parallel data of all three Swiss sign languages. |
| Outcome: | The proposed corpus contains parallel sign language videos and spoken language subtitles. |
Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models (2025.findings-emnlp)
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| Challenge: | grammatical gender significantly influences image generation in text-to-image models . masculine grammatikal markers increase male representation to 73% on average . feminine grammatological markers increase female representation to 38% . |
| Approach: | They propose a cross-linguistic benchmark examining words where grammatical gender contradicts stereotypical gender associations. |
| Outcome: | The proposed benchmark examines words where grammatical gender contradicts stereotypical gender associations. |
Textual Coverage of Eventive Entries in Lexical Semantic Resources (2024.lrec-main)
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| Challenge: | Several English, German, Spanish and Czech lexical semantic resources (which, for the most part, focus on verbs and predicates) have been selected for this experiment. |
| Approach: | They propose to quantify coverage gaps in lexical semantic resources when applied to running texts taken from the internet. |
| Outcome: | The proposed resources cover eventive entries (verbs, predicates, etc.) of well-known lexical semantic resources when applied to running texts taken from the internet. |
Extracting Linguistic Information from Large Language Models: Syntactic Relations and Derivational Knowledge (2025.emnlp-main)
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| Challenge: | Using large language models, we study their morphosyntactic competence and generalization capabilities. |
| Approach: | They propose to use morphosyntactic tasks to study their linguistic knowledge and generalization capabilities to extract different types of morphological structure for typologically diverse languages. |
| Outcome: | The proposed models outperform GPT-4o and LLaMA 3.3-70B in all diagnostic tasks, but show little evidence of abstract morphological rule learning. |
Towards the Roots of the Negation Problem: A Multilingual NLI Dataset and Model Scaling Analysis (2025.findings-emnlp)
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| Challenge: | Negations are key to determining sentence meaning, making them essential for logical reasoning. |
| Approach: | They construct and publish two new textual entailment datasets in four languages with paired examples differing in negation. |
| Outcome: | The results show that increasing the model size may improve the models’ ability to handle negations. |
Low-Resource Languages LLM Disinformation is Within Reach: The Case of Walliserdeutsch (2025.findings-emnlp)
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| Challenge: | a low-resource language lacks fluidity, but its capabilities can be leveraged. |
| Approach: | They investigate whether a moderately sophisticated attacker can perform an impersonation attack in the Walliserdeutsch dialect . |
| Outcome: | The proposed attack is performed in the Walliserdeutsch dialect, a low-resource language . the findings highlight the urgency of LLM detectability research in low-source languages. |
BOUQuET : dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation (2025.emnlp-main)
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Pierre Andrews, Mikel Artetxe, Mariano Coria Meglioli, Marta R. Costa-jussà, Joe Chuang, David Dale, Mark Duppenthaler, Nathanial Paul Ekberg, Cynthia Gao, Daniel Edward Licht, Jean Maillard, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Eduardo Sánchez, Ioannis Tsiamas, Arina Turkatenko, Albert Ventayol-Boada, Shireen Yates
| Challenge: | BOUQUET is a multi-way, multicentric and multi-register/domain dataset and benchmark . the dataset is handcrafted in 8 non-English languages . |
| Approach: | They propose to use BOUQuET to collect a multi-way, multicentric and multi-register/domain dataset and benchmark in 8 non-English languages. |
| Outcome: | The proposed dataset is available at https://huggingface.co/datasets/facebook/bouquet. |
Towards Equitable Natural Language Understanding Systems for Dialectal Cohorts: Debiasing Training Data (2024.lrec-main)
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| Challenge: | Prior research has shown that biases exist in these models against certain languages or dialects. |
| Approach: | They propose to use a dialect identification model to obtain targeted training data augmentation for under-represented dialects to debias NLU model for dialectal cohorts in NLU systems. |
| Outcome: | The proposed framework can provide insights on dialect disparity in real-world NLU systems and targeted data argumentation can help narrow the model’s performance gap between standard language speakers and dialect speakers. |
Can AI-Generated Persuasion Be Detected? Persuaficial Benchmark and AI vs. Human Linguistic Differences (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) can generate highly persuasive text, raising concerns about misuse for propaganda, manipulation, and other harmful purposes. |
| Approach: | They propose a multilingual benchmark to compare LLM-generated persuasive texts with human-written ones. |
| Outcome: | The proposed benchmark compares human-authored and LLM-generated persuasive texts . it finds that overtly persuasive LLMs are easier to detect than human-written ones . |
Assessing Reliability and Political Bias In LLMs’ Judgements of Formal and Material Inferences With Partisan Conclusions (2025.acl-long)
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| Challenge: | This paper examines the ability of LLMs to correctly label simple inferences with partisan conclusions. |
| Approach: | They develop a dataset with formal and material inferences with conclusions that favor either the political left or the political right. |
| Outcome: | The proposed models show that they are unreliable and political bias persists throughout the English and German datasets. |
It’s Not a Walk in the Park! Challenges of Idiom Translation in Speech-to-text Systems (2025.acl-long)
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| Challenge: | idioms are defined as words with a figurative meaning not deducible from their individual components. |
| Approach: | They compare idiom translation as compared to conventional news translation in two languages . they compare MT and SLT systems with MT, Large Language Models and cascaded alternatives . |
| Outcome: | The proposed systems show better handling of idioms than standard news translation systems. |
The Harmonic Structure of Information Contours (2025.acl-long)
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Eleftheria Tsipidi, Samuel Kiegeland, Franz Nowak, Tianyang Xu, Ethan Wilcox, Alex Warstadt, Ryan Cotterell, Mario Giulianelli
| Challenge: | Language typically does not maintain a uniform information rate, but it fluctuates around a global average . a new study suggests periodicity may be a factor in information rate oscillations . |
| Approach: | They propose a hypothesis that language does not maintain a uniform information rate . they apply harmonic regression and introduce a new extension to detect periodicity . |
| Outcome: | The proposed method reveals that language oscillates at periodic intervals across frequencies . it also offers a framework for uncovering structural pressures at various levels of linguistic granularity. |
Zero-shot Cross-lingual Automated Essay Scoring (2024.lrec-main)
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| Challenge: | Existing approaches to automate essay scoring (AES) use pre-trained multilingual representations and writing quality alignment to score essays in unseen languages. |
| Approach: | They propose a novel cross-lingual scoring method using pretrained multilingual representation and writing quality alignment to represent multilingual essays. |
| Outcome: | The proposed method achieves state-of-the-art cross-lingual scoring performance. |
LangMark: A Multilingual Dataset for Automatic Post-Editing (2025.acl-long)
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Diego Velazquez, Mikaela Grace, Konstantinos Karageorgos, Lawrence Carin, Aaron Schliem, Dimitrios Zaikis, Roger Wechsler
| Challenge: | Automated post-editing (APE) aims to correct errors in machine-translated text . lack of large-scale multilingual datasets specifically tailored to NMT outputs hinders APE development . |
| Approach: | They propose to use a human-annotated multilingual APE dataset for English translation to seven languages to address this gap. |
| Outcome: | The proposed dataset offers both linguistic diversity and scale. |
Multi-LMentry: Can Multilingual LLMs Solve Elementary Tasks Across Languages? (2025.emnlp-main)
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Luca Moroni, Javier Aula-Blasco, Simone Conia, Irene Baucells, Naiara Perez, Silvia Paniagua Suárez, Anna Sallés, Malte Ostendorff, Júlia Falcão, Guijin Son, Aitor Gonzalez-Agirre, Roberto Navigli, Marta Villegas
| Challenge: | a recent study focused on complex, high-level tasks, but LMentry is limited to English . a multilingual evaluation of large language models is needed to address this gap, authors say . |
| Approach: | They propose a compact benchmark that enables systematic evaluation of large language models . they propose to use tasks that are trivial for humans but remain surprisingly difficult for LLMs . |
| Outcome: | The proposed benchmark is limited to English, leaving its insights linguistically narrow. |
GerAV: Towards New Heights in German Authorship Verification using Fine-Tuned LLMs on a New Benchmark (2026.findings-acl)
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| Challenge: | Authorship verification (AV) is a task of determining whether two texts were written by the same author. |
| Approach: | They propose a benchmark for German AV comprising over 400k labeled text pairs. |
| Outcome: | The proposed model outperforms baselines and state-of-the-art models by 0.09 and surpasses GPT-5 in a zero-shot setting by 0.08. |