Updating bilingual dictionary entries is a tedious, time-consuming, and highly subjective task, especially when a new sense in the source language requires identifying an appropriate translation equivalent. To date, there have been no attempts to automatize the discovery of new bilingual sense entries. Related tasks such as Word-level Bilingual Dictionary Induction and cross-lingual embedding alignment do not account for polysemy and are not applied to lexicographic data. In contrast to their monolingual counterparts, bilingual dictionaries fall short in terms of completeness, detail with respect to examples and glosses, and diachronic information. We introduce a novel NLP task, Sense-Level Bilingual Dictionary Induction (SenseBDI), at the intersection of lexical semantics, cross-lingual, and diachronic NLP. We construct a dataset of time-stamped sense-level bilingual dictionary entries by aligning two bilingual dictionaries, two monolingual dictionaries, and the multilingual resource BabelNet, thereby enriching bilingual entries with monolingual source-language information. We propose a baseline based on nearest-neighbor search over cross-lingual embeddings of glosses and usages. We find that usages contribute more strongly than glosses, with substantial variation across language pairs and discuss task-specific challenges with regards to target language polysemy and future directions such as transfer to real-world scenarios.

This study investigates semantic changes in four historically loaded German political lexemes: Volk, Nation, Staat, and Land. These terms denote basic categories of peoplehood, national community, institutional statehood, and territorial belonging, but their use and emotional associations were reshaped by the ideological language of National Socialism and by the political reconstruction of Germany after 1945. Using DWDS as the empirical basis, we compare a pre-war and wartime baseline (1918–1945) with the post-war period (1945–2024). Our study combines frequency analysis, collocation analysis, and LLM-assisted annotation along three dimensions: nationalist intensity, emotional valence, and temporality. The results suggest different trajectories across the four lexemes: Staat and Land remain comparatively stable as institutional and territorial terms, while Volk shows clearer signs of contextual restructuring, semantic distancing, and possible renewed political markedness. Nation displays intermediate patterns shaped by historical rupture, reunification, and contemporary political discourse. By integrating corpus-based semantic analysis with conceptual history, our study contributes empirical evidence to debates on post-war German national identity and the linguistic reconfiguration of political concepts

We present MULTITEMPBENCH, a multilingual temporal reasoning benchmark spanning three tasks, date arithmetic, time zone conversion, and temporal relation extraction across five languages (English, German, Chinese, Arabic, and Hausa) and multiple calendar conventions (Gregorian, Hijri, and Chinese Lunar). MULTITEMPBENCH contains 15,000 examples built by translating 750 curated English questions and expanding each into controlled date-format variants. We evaluate 20 LLMs and introduce the multilingual Date Fragmentation Ratio (mDFR), calibrated with human severity ratings, together with geometric-probing analyses of internal temporal representations. We find tokenisation quality of temporal artefacts is a resource-dependent bottleneck: in low-resource languages and rarer calendar formats, fragmentation disrupts Year/Month/Day separation and accuracy collapses, while high-resource settings are often robust to digit-level splitting. Beyond tokenisation, crossed mixed-effects regression shows that temporal linearity is the strongest predictor of temporal reasoning in high-resource languages, whereas fragmentation is the stronger predictor in low-resource languages.

Large language models (LLMs) are increasingly used for creative tasks such as literary translation. Yet translational creativity remains underexplored and is rarely evaluated at scale, while source-text comprehension is typically studied in isolation, despite the fact that, in professional translation, comprehension and creativity are tightly intertwined. We address these gaps with a paired-task framework applied to literary excerpts from 11 books. Task 1 assesses source-text comprehension, and Task 2 evaluates translational creativity through Units of Creative Potential (UCPs), such as metaphors and wordplay. Using a scalable evaluation setup that combines expert human annotations with UCP-based automatic scoring, we benchmark 23 models and four creativity-oriented prompts. Our findings show that strong comprehension does not translate into human-level creativity: models often produce literal or contextually inappropriate renderings, with particularly large gaps for the more distant English–Chinese language pair. Creativity-oriented prompts yield only modest gains, and only one model, Mistral-Large, comes close to human-level creativity (0.167 vs. 0.246). Across all model–prompt combinations, only three exceed a creativity score of 0.1, while the rest remain at or near zero.

Despite remarkable progress in machine translation (MT), non-AI communities have raised growing concerns about MT systems, suggesting a noticeable gap between technical advancement and the needs of real-world users. For instance, while NLP researchers focus on benchmark performance, end users care about ethical concerns, trust, reliability, costs, and more. We argue that listening to various user communities is essential so that research efforts would be directed towards the problems that the communities care about. To this end, we present a large-scale analysis, for the first time, that investigates what four stakeholder communities (AI developers, professional translators, language learners, and language service providers) post about MT technology on social media. To do so, we construct a dataset of 79,286 posts and comments from Reddit, Facebook, Bluesky, and Mastodon from 2019 to 2025, and analyse where these communities disagree, and how and why. Overall, we find that communities often disagree, and even show strong conflicts due to polarised sentiments on topics such as translation quality, efficiency, and reliability. This is because these communities approach these topics differently: the AI community frames them as technical and computational problems, while non-AI (user) communities care more about quality nuances, time savings, user trust, and broader social issues.

Sign language lexicographers construct bilingual dictionaries by establishing word-to-sign mappings, where polysemous and homonymous words corresponding to different signs across contexts are often underrepresented. A usage-based approach examining how word senses map to signs can identify such novel mappings absent from current dictionaries, enriching lexicographic resources.We address this by analyzing German and German Sign Language (Deutsche Gebärdensprache, DGS), manually annotating 1,404 word use–to–sign ID mappings derived from 32 words from the German Word Usage Graph (D-WUG) and 49 signs from the Digital Dictionary of German Sign Language (DW-DGS). We identify three correspondence types: Type 1 (one-to-many), Type 2 (many-to-one), and Type 3 (one-to-one), plus No Match cases. We evaluate computational methods: Exact Match (EM) and Semantic Similarity (SS) using SBERT embeddings. SS substantially outperforms EM overall 88.52% vs. 71.31%), with dramatic gains for Type 1 (+52.1 pp). Our work establishes the first annotated dataset for cross-modal sense correspondence and reveals which correspondence patterns are computationally tractable.Our code and dataset are made publicly available

Recent research has focused on literary machine translation (MT) as a new challenge in MT. However, the evaluation of literary MT remains an open problem. We contribute to this ongoing discussion by introducing LITEVAL-CORPUS, a paragraph-level parallel corpus containing verified human translations and outputs from 9 MT systems, which totals over 2k translations and 13k evaluated sentences across four language pairs, costing 4.5k€. This corpus enables us to (i) examine the consistency and adequacy of human evaluation schemes with various degrees of complexity, (ii) compare evaluations by students and professionals, assess the effectiveness of (iii) LLM-based metrics and (iv) LLMs themselves. Our findings indicate that the adequacy of human evaluation is controlled by two factors: the complexity of the evaluation scheme (more complex is less adequate) and the expertise of evaluators (higher expertise yields more adequate evaluations). For instance, MQM (Multidimensional Quality Metrics), a complex scheme and the de facto standard for non-literary human MT evaluation, is largely inadequate for literary translation evaluation: with student evaluators, nearly 60% of human translations are misjudged as indistinguishable or inferior to machine translations. In contrast, BWS (BEST-WORST SCALING), a much simpler scheme, identifies human translations at a rate of 80-100%. Automatic metrics fare dramatically worse, with rates of at most 20%. Our overall evaluation indicates that published human translations consistently outperform LLM translations, where even the most recent LLMs tend to produce considerably more literal and less diverse translations compared to humans.

Modern BPE tokenisers often split calendar dates into meaningless fragments, e.g., “20250312” → “202”, “503”, “12”, inflating token counts and obscuring the inherent structure needed for robust temporal reasoning. In this work, we (1) introduce a simple yet interpretable metric, termed date fragmentation ratio, that measures how faithfully a tokeniser preserves multi-digit date components; (2) release DateAugBench, a suite of 6500 examples spanning three temporal reasoning tasks: context-based date resolution, format-invariance puzzles, and date arithmetic across historical, contemporary, and future time periods; and (3) through layer-wise probing and causal attention-hop analyses, uncover an emergent date-abstraction mechanism whereby large language models stitch together the fragments of month, day, and year components for temporal reasoning. Our experiments show that excessive fragmentation correlates with accuracy drops of up to 10 points on uncommon dates like historical and futuristic dates. Further, we find that the larger the model, the faster the emergent date abstraction heals date fragments. Lastly, we observe a reasoning path that LLMs follow to assemble date fragments, typically differing from human interpretation (year → month → day).

The impact of Large Language Models (LLMs) has extended into literary domains. However, existing evaluation metrics for literature prioritize mechanical accuracy over artistic expression and tend to overrate machine translation as being superior to human translation from experienced professionals. In the long run, this bias could result in an irreversible decline in translation quality and cultural authenticity. In response to the urgent need for a specialized literary evaluation metric, we introduce LITRANSPROQA, a novel, reference-free, LLM-based question-answering framework designed for literary translation evaluation. LITRANSPROQA integrates humans in the loop to incorporate insights from professional literary translators and researchers, focusing on critical elements in literary quality assessment such as literary devices, cultural understanding, and authorial voice. Our extensive evaluation shows that while literary-finetuned XCOMET-XL yields marginal gains, LITRANSPROQA substantially outperforms current metrics, achieving up to 0.07 gain in correlation and surpassing the best state-of-the-art metrics by over 15 points in adequacy assessments. Incorporating professional translator insights as weights further improves performance, highlighting the value of translator inputs. Notably, LITRANSPROQA reaches an adequacy performance comparable to trained linguistic student evaluators, though it still falls behind experienced professional translators. LITRANSPROQA shows broad applicability to open-source models like LLaMA3.3-70b and Qwen2.5-32b, indicating its potential as an accessible and training-free tool for evaluating literary translations that require local processing due to copyright or ethical considerations.

Despite the predominance of contextualized embeddings in NLP, approaches to detect semantic change relying on these embeddings and clustering methods underperform simpler counterparts based on static word embeddings. This stems from the poor quality of the clustering methods to produce sense clusters—which struggle to capture word senses, especially those with low frequency. This issue hinders the next step in examining how changes in word senses in one language influence another. To address this issue, we propose a graph-based clustering approach to capture nuanced changes in both high- and low-frequency word senses across time and languages, including the acquisition and loss of these senses over time. Our experimental results show that our approach substantially surpasses previous approaches in the SemEval2020 binary classification task across four languages. Moreover, we showcase the ability of our approach as a versatile visualization tool to detect semantic changes in both intra-language and inter-language setups. We make our code and data publicly available.