Papers with Hebrew
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. |
AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level (2022.acl-long)
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| Challenge: | a recent study shows that large pre-trained language models are not sufficient for Hebrew. |
| Approach: | They propose a large pre-trained language model for Hebrew that recovers morphological segments encoded in contextualized embedding vectors. |
| Outcome: | The proposed model obtains state-of-the-art on all tasks beyond contemporary Hebrew baselines. |
“Talk to me with left, right, and angles”: Lexical entrainment in spoken Hebrew dialogue (2021.eacl-main)
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| Challenge: | entrainment is a widespread phenomenon in human interaction that leads interlocutors to adapt their linguistic productions to become more similar to each other. |
| Approach: | They propose to use existing measures to analyze Hebrew speakers interacting in a Map Task to find evidence of lexical entrainment. |
| Outcome: | The proposed study is the first to examine lexical entrainment in Hebrew using two existing measures. |
Nakdan: Professional Hebrew Diacritizer (2020.acl-demos)
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| Challenge: | a system for automatic diacritization of Hebrew Text is available for both casual and expert users. |
| Approach: | They propose a system for automatic diacritization of Hebrew Text . the system combines declarative linguistic knowledge with machine learning models . |
| Outcome: | The proposed system is available for both casual and expert users. |
A Dataset for Metaphor Detection in Early Medieval Hebrew Poetry (2024.eacl-short)
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| Challenge: | a corpus of late antique and medieval Hebrew poetry is rich in metaphors and similes . scholars in the humanities need to distinguish between figurative and literal language . |
| Approach: | They present a corpus of late antique and medieval Hebrew poetry with expert annotations of metaphor . they hope to facilitate further research in this area . |
| Outcome: | The proposed dataset includes a corpus of late antique and medieval Hebrew poetry with expert annotations of metaphor. |
Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora (2020.acl-main)
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| Challenge: | comparing two corpus texts and searching for words that differ in their usage between them is a common problem in digital humanities and computational social science. |
| Approach: | They propose an alternative approach that does not use vector space alignment, and instead considers the neighbors of each word. |
| Outcome: | The proposed method is interpretable and stable in 9 different setups and is highly reliable. |
Design Challenges in Named Entity Transliteration (C18-1)
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| Challenge: | Named entity transliteration is an important component in many search and language understanding tasks. |
| Approach: | They empirically evaluate a named entity transliteration task using traditional methods . they use a stack of convolutional layers to create a neural network with a new approach . |
| Outcome: | The proposed system outperforms two neural approaches in the named entity transliteration task. |
Neural Modeling for Named Entities and Morphology (NEMO2) (2021.tacl-1)
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| Challenge: | Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens. |
| Approach: | They develop a morphologically rich-and-ambiguous language with a token-level and morpheme-level NER annotation framework to address Named Entity Recognition (NER) a novel hybrid architecture precedes and prunes morphology and outperforms the standard pipeline for Hebrew NER and Hebrew morphologies. |
| Outcome: | The proposed architecture outperforms the standard pipeline for Hebrew NER and Hebrew morphological decomposition tasks. |
Compositional Generalization in Multilingual Semantic Parsing over Wikidata (2022.tacl-1)
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| Challenge: | Semantic parsers are mostly designed for and evaluated on English resources, such as CFQ. |
| Approach: | They propose a method for creating a multilingual, parallel question-query dataset . they analyze compositional generalization of parsers in Hebrew, Kannada, Chinese, and English . |
| Outcome: | The proposed method analyzes compositional generalization of parsers in Hebrew, Kannada, Chinese, and English. |
Embible: Reconstruction of Ancient Hebrew and Aramaic Texts Using Transformers (2024.findings-eacl)
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| Challenge: | inscriptions are damaged over time due to earthquakes, fires, political conflicts, and other natural and human-related causes. |
| Approach: | They evaluate masked language modeling approach to reconstruct illegible inscriptions . they use transformer-based models to fine-tune models on Biblical texts . |
| Outcome: | The proposed model achieves the highest text completion accuracy on masked parts of the Biblical texts. |
Do Pretrained Contextual Language Models Distinguish between Hebrew Homograph Analyses? (2023.eacl-main)
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| Challenge: | Semitic morphologically-rich languages are characterized by extreme word ambiguity . many of the words are homographs with multiple possible analyses . |
| Approach: | They evaluate existing models for Hebrew homographs using word-piece embeddings . they find they are more effective when the number of word-part splits is limited . |
| Outcome: | The proposed models outperform non-contextualized embeddings on Hebrew homograph challenge sets. |
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. |
A Truly Joint Neural Architecture for Segmentation and Parsing (2024.eacl-long)
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| Challenge: | Contemporary multilingual dependency parsers can parse a diverse set of languages, but performance is lower for Morphologically Rich Languages. |
| Approach: | They propose a joint neural architecture where a lattice-based representation is provided to an arc-factored model and solves the morphological segmentation and syntactic parsing tasks at once. |
| Outcome: | The proposed architecture is language-agnostic and language-based to improve on Hebrew . it shows that the proposed model can parse morphological segmentation and syntactic parsing tasks at once. |
A Tale of Two Scripts: Transliteration and Post-Correction for Judeo-Arabic (2026.eacl-long)
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| Challenge: | Thousands of JA texts are available online, covering genres such as philosophy, biblical commentary, and Bible translations. |
| Approach: | They propose a two-step approach to automatically transliterate Judeo-Arabic into Arabic script using simple character-level mapping followed by post-correction to address grammatical and orthographic errors. |
| Outcome: | The proposed method enables Arabic NLP tools to perform morphosyntactic tagging and machine translation, which would have been impossible on the original texts. |
Code-Switching and Back-Transliteration Using a Bilingual Model (2024.findings-eacl)
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| Challenge: | a wealth of Judeo-Arabic texts is available on the internet, but most speakers of Arabic are unfamiliar with the Hebrew script. |
| Approach: | They propose two machine-learning models for transliterating Judeo-Arabic into Arabic . they also propose a model for identifying non-Arabian words, predominantly Hebrew and Aramaic . |
| Outcome: | The proposed models outperform previous solutions for transliterating Judeo-Arabic into Arabic and identifying non-Arabian words, mainly Hebrew and Aramaic. |
Colorless Green Recurrent Networks Dream Hierarchically (N18-1)
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| Challenge: | Recurrent neural networks (RNNs) can induce non-trivial properties of language. |
| Approach: | They investigate whether RNNs can track hierarchical syntactic structure . they include nonsensical sentences where RNN cannot rely on semantic cues . |
| Outcome: | The proposed models can predict long-distance agreement in nonsensical sentences in Italian and English. |
Effective QA-Driven Annotation of Predicate–Argument Relations Across Languages (2026.eacl-long)
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| Challenge: | Explicit representations of predicate-argument relations are a cornerstone of natural language understanding. |
| Approach: | They propose a cross-linguistic projection approach that reuses an English QA-SRL parser within a constrained translation and word-alignment pipeline to automatically generate question-answer annotations aligned with target-language predicates. |
| Outcome: | The proposed approach outperforms strong multilingual LLMs in Hebrew, Russian, and French. |
A Multi-Orthography Parallel Corpus of Yiddish Nouns (2020.lrec-1)
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| Challenge: | Existing corpora of Yiddish text are limited to a single, potentially non-standard orthography . non-phonetically spelled Hebrew words are the largest cause of error, according to our study . |
| Approach: | They propose a multi-orthography parallel Yiddish corpus based on Wiktionary scraping . they also demonstrate how the system can be used to bootstrap a transliteration model . |
| Outcome: | The proposed system achieves error rates between 16.79% and 28.47% on the test set. |
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology (P19-1)
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| Challenge: | Gender stereotypes are manifest in most of the world's languages and are consequently propagated or amplified by NLP systems. |
| Approach: | They propose a method for converting between masculine-inflected and feminine-infflectes sentences in morphologically rich languages to reduce gender stereotyping by a factor of 2.5 without any sacrifice to grammaticality. |
| Outcome: | The proposed approach reduces gender stereotyping by 2.5 without any sacrifice to grammaticality. |
Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from Modern Hebrew (C18-1)
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| Challenge: | Existing sentiment analyzers for MRLs that use tokens and morpheme-based representations have no empirically studied effects of representation choices on neural sentiment analysis. |
| Approach: | They develop a sentiment analysis benchmark for Hebrew based on 12K social media comments and provide two instances of data. |
| Outcome: | The proposed benchmarks show that representation choices have measurable effects on task perfromance and that they vary depending on architecture type. |
Automatic Thesaurus Construction for Modern Hebrew (L18-1)
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| Challenge: | Modern Hebrew lacks lexical resources fundamental to many natural language processing tools. |
| Approach: | They propose a method for generating a cooccurrence based thesaurus in a MRL and a distributional similarity method for Hebrew. |
| Outcome: | The proposed method is not optimal for modern Hebrew, the authors show . they used Hebrew WordNet as their gold standard for the analysis . |
Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs (2026.eacl-long)
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Alireza Dehghanpour Farashah, Aditi Khandelwal, Marylou Fauchard, Zhuan Shi, Negar Rostamzadeh, Golnoosh Farnadi
| Challenge: | Existing studies on unlearning in multilingual large language models focus on monolingual settings, typically English. |
| Approach: | They propose to use a multilingual data and concept unlearning model to investigate the problem . they extend benchmarks for factual knowledge and stereotypes into ten languages . |
| Outcome: | The proposed model is able to unlearning in 10 languages across five languages and resource levels. |
Cross-Lingual UMLS Named Entity Linking using UMLS Dictionary Fine-Tuning (2022.findings-acl)
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| Challenge: | a new method for named entity linking is being developed in the field of public health . it uses an offline unsupervised construction of a translated dictionary and a pre-trained transformer language model to filter candidates according to context. |
| Approach: | They propose a method for mapping mentions in a source language to UMLS concepts . they extend an offline unsupervised translation of a translated UMLS dictionary . |
| Outcome: | The proposed approach achieves state-of-the-art on the Hebrew Camoni corpus and English datasets. |
MRL Parsing Without Tears: The Case of Hebrew (2024.findings-acl)
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| Challenge: | a new approach to parsing morphologically rich languages (MRLs) is needed to overcome the deficiencies of current approaches. |
| Approach: | They propose a "flipped pipeline" where multiple layers are predicted independently on whole-token basis and then synthesized. |
| Outcome: | The proposed model achieves near-SOTA performance on Hebrew NLP tasks. |
Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages (2026.findings-eacl)
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| Challenge: | Recent studies suggest that summarization in English may be solved, or even "dead" However, there are no accessible, high-quality summarizing datasets in under-represented languages. |
| Approach: | They propose a method for collecting naturally occurring summaries via front-page teasers, where editors summarize full length articles. |
| Outcome: | The proposed method is suited to varying linguistic resources and is available in seven languages. |
A Second Wave of UD Hebrew Treebanking and Cross-Domain Parsing (2022.emnlp-main)
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| Challenge: | Foundational Hebrew NLP tasks have relied on various versions of the Hebrew Treebank . however, the data in the HTB is now over 30 years old and does not cover many aspects of contemporary Hebrew on the web. |
| Approach: | They propose to use Hebrew Wikipedia to stratify the text from a UD treebank. |
| Outcome: | The proposed treebank is based on a single-source newswire corpus selected from Hebrew Wikipedia. |
ISO-based Annotated Multilingual Parallel Corpus for Discourse Markers (2022.lrec-1)
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Purificação Silvano, Mariana Damova, Giedrė Valūnaitė Oleškevičienė, Chaya Liebeskind, Christian Chiarcos, Dimitar Trajanov, Ciprian-Octavian Truică, Elena-Simona Apostol, Anna Baczkowska
| Challenge: | Discourse markers carry information about the discourse structure and organization, and also signal local dependencies or epistemic stance of speaker. |
| Approach: | They propose an ISO-based annotated multilingual parallel corpus for discourse markers . they propose an annotation scheme for discourse relations with a plug-in to ISO 24617-2 . |
| Outcome: | The proposed language resource is based on an ISO-based annotated multilingual parallel corpus of discourse markers. |
A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration (2020.findings-emnlp)
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| Challenge: | morphological parsers face a formidable challenge with unbalanced ambiguities in homographs . case of unbalanciated ambiguity is difficult to disambiguate, especially in cases of unbalancing . a new dataset improves the overall average F1 score for Hebrew homograph . |
| Approach: | They propose a challenge set for Hebrew homographs with substantial attestation of each analysis of 21 Hebrew homographies. |
| Outcome: | The proposed set improves the average F1 score for Hebrew homographs by 0.67 . the annotated datasets are made publicly available for further research. |
Nunc profana tractemus. Detecting Code-Switching in a Large Corpus of 16th Century Letters (2022.lrec-1)
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Martin Volk, Lukas Fischer, Patricia Scheurer, Bernard Silvan Schroffenegger, Raphael Schwitter, Phillip Ströbel, Benjamin Suter
| Challenge: | a corpus of 16th century letters from and to the Zurich reformer Heinrich Bullinger has been preserved . a recent study investigated code-switching in these 8600 letters . |
| Approach: | They investigate the automatic detection of code-switching in a 16th century letter exchange . they use a popular language identifier to bootstrap a word-based language classifier . |
| Outcome: | The proposed language classifier bootstraps with a popular identifier on a small training corpus of 150 sentences per language. |
Better, Faster, Stronger Sequence Tagging Constituent Parsers (N19-1)
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| Challenge: | Existing efforts to speed up constituent parsing have focused on chart-based or shift-reduce parsers. |
| Approach: | They propose to use auxiliary losses and sentence-level fine-tuning to mitigate greedy decoding issues. |
| Outcome: | The proposed model surpasses the performance of sequence tagging constituent parsers on the English and Chinese Penn Treebank datasets and reduces their parsing time even further. |
HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew (2024.findings-acl)
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| Challenge: | Large language models excel in various natural language tasks in English, but their performance in low-resource languages like Hebrew remains unclear. |
| Approach: | They propose a benchmark dataset specifically designed for Hebrew abstractive text summarization that combines 10,000 article-summary pairs from Hebrew news websites. |
| Outcome: | The proposed dataset shows that it presents distinct difficulties even for state-of-the-art LLMs. |
A multilabel approach to morphosyntactic probing (2021.findings-emnlp)
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| Challenge: | Morphologically rich languages present unique challenges to natural language processing . morphological supervision can improve the quality of multilingual language models . |
| Approach: | They propose a multilabel probing task to assess morphosyntactic representations of multilingual word embeddings. |
| Outcome: | The proposed probing task makes it easy to explore morphosyntactic representations . it also allows the study of how language models handle co-occurring features . |
A Pointer Network Architecture for Joint Morphological Segmentation and Tagging (2020.findings-emnlp)
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| Challenge: | Morphological Disambiguation (MD) is a task of decomposing tokens into morphemes . a simple pipeline is used to segment and tagging raw tokens . |
| Approach: | They propose a new pointer network model that combines symbolic knowledge of morphemes with the learning capacity of neural end-to-end modeling. |
| Outcome: | The proposed model outperforms all previous reported results on Hebrew and Turkish . it uses morphological knowledge and the learning capacity of neural end-to-end modeling . |
Offensive language detection in Hebrew: can other languages help? (2022.lrec-1)
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| Challenge: | Various approaches for offensive language detection have been applied for this task . contamination of social networks with offensive content is a new reality affecting almost all of us . |
| Approach: | They propose to use multiple supervised models and text representations to detect offensive language in three languages, including two Semitic languages. |
| Outcome: | The proposed model can detect offensive content in two Semitic languages, including Hebrew and Arabic, and it is able to perform cross-lingual and multilingual learning. |
LCHAIM - Investigating Long Context Reasoning in Hebrew (2025.findings-acl)
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| Challenge: | a dataset designed to evaluate long premises and complex reasoning models in Hebrew is being used in research. |
| Approach: | They introduce a dataset to evaluate Hebrew NLI models on long premises and complex reasoning tasks. |
| Outcome: | The proposed dataset evaluates models on tasks involving long premises and complex reasoning. |
From Pattern to Interpretation. Using Colibri Core to Detect Translation Patterns in the Peshitta. (2022.lrec-1)
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| Challenge: | Using Colibri Core to detect translation patterns in the Hebrew Bible and its Syriac source text is a promising approach, but does not allow the creation of a bilingual model. |
| Approach: | They propose to use Colibri Core to detect n-gram and skipgram patterns in the Hebrew Bible and its Syriac source text. |
| Outcome: | The proposed modeller can detect n-gram and skipgram patterns in both Hebrew and Syriac texts without the need for textual annotations. |
HeGeL: A Novel Dataset for Geo-Location from Hebrew Text (2023.findings-acl)
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| Challenge: | Existing datasets in English for textual geolocation are limited because of the location of the place is implicit. |
| Approach: | They propose to use a Hebrew place description corpus to analyze lingual geospatial reasoning. |
| Outcome: | The Hebrew Geo-Location corpus collects literal Hebrew place descriptions and analyzes lingual geospatial reasoning. |
Multilingual Sequence-to-Sequence Models for Hebrew NLP (2023.findings-acl)
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| Challenge: | Recent work on pretrained language models for Hebrew is under-parameterized and under-trained . previous work on pretraining Hebrew LMs focused on encoder-only architectures . |
| Approach: | They propose to use sequence-to-sequence generative architectures to train large LMs in morphologically rich languages such as Hebrew. |
| Outcome: | The proposed model improves on all existing Hebrew NLP benchmarks. |
Beyond Word Boundaries: A Hebrew Coreference Benchmark and an Evaluation Protocol for Morphologically Complex Text (2026.acl-long)
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| Challenge: | CR methods originally designed for English struggle with Morphologically Rich Languages (MRLs) a single token in Hebrew may consist of multiple anaphors, and word/morpheme boundary discrepancies make mention detection and coreference resolution difficult in MRLs. |
| Approach: | They propose a CR dataset that identifies mentions at word, sub-word and multi-word levels and an evaluation protocol that directly addresses word/morpheme boundary discrepancies. |
| Outcome: | The proposed evaluation protocol directly addresses word/morpheme boundary discrepancies in Modern Hebrew, an MRL rich with complex words and pronominal clitics. |
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. |
HebID: Detecting Social Identities in Hebrew-language Political Text (2025.findings-emnlp)
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| Challenge: | Existing NLP datasets focus on coarse-grained identity categories . existing datasets are mostly English-centric and focus on fine-grain categories based on cultural contexts. |
| Approach: | They introduce the first multilabel Hebrew corpus for social identity detection . they use Hebrew-tuned encoders alongside 2B-9B-parameter decoders . |
| Outcome: | The proposed classifier is based on a national public survey and uses Hebrew-tuned encoders to analyze political discourse and political speeches. |
Probing for Reading Times (2026.acl-long)
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Eleftheria Tsipidi, Samuel Kiegeland, Francesco Ignazio Re, Tianyang Xu, Mario Giulianelli, Karolina Stanczak, Ryan Cotterell
| Challenge: | a large body of work on probing has demonstrated that language model representations encode a wealth of linguistic information, but it remains unclear whether they also capture cognitive signals about human processing. |
| Approach: | They use regularized linear regression to compare language model representations against scalar predictors. |
| Outcome: | The representations from early layers outperform surprisal in predicting early-pass measures such as first fixation and gaze duration. |
Comparing Biases and the Impact of Multilingual Training across Multiple Languages (2023.emnlp-main)
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Sharon Levy, Neha John, Ling Liu, Yogarshi Vyas, Jie Ma, Yoshinari Fujinuma, Miguel Ballesteros, Vittorio Castelli, Dan Roth
| Challenge: | Currently, studies on bias and fairness in natural language processing focus on a single language and/or across few attributes (e.g. gender, race). However, biases can manifest differently across languages for individual attributes. |
| Approach: | They adapt existing sentiment bias templates in English to Italian, Chinese, Hebrew, and Spanish for race, religion, nationality, and gender. |
| Outcome: | The proposed model favors groups that are dominant in each language's culture, indicating bias amplification, after multilingual finetuning. |
From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages (MRLs)? (2020.acl-main)
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| Challenge: | a decade has passed since the establishment of SPMRL to address the peculiar challenges of Statistical Parsing for Morphologically-rich languages (MRLs). |
| Approach: | They propose a framework for parsing MRLs and propose implementing symbolic ideas into modern neural architectures. |
| Outcome: | The proposed strategies are based on the multi-tagging task in Hebrew, a morphologically-rich, high-fusion, language. |
Building a Hebrew Semantic Role Labeling Lexical Resource from Parallel Movie Subtitles (2020.lrec-1)
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| Challenge: | Existing semantic role labeling resources for Hebrew are not available in English. |
| Approach: | They propose a semantic role labeling resource for Hebrew built semi-automatically through annotation projection from English to Hebrew. |
| Outcome: | The proposed resource is built semi-automatically from an English dataset . it includes morphological analysis, dependency syntax and semantic role labeling . |
HeQ: a Large and Diverse Hebrew Reading Comprehension Benchmark (2023.findings-emnlp)
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| Challenge: | Current benchmarks for Hebrew Natural Language Processing (NLP) focus mainly on morpho-syntactic tasks, neglecting the semantic dimension of language understanding. |
| Approach: | They propose to use Hebrew machine reading comprehension (MRC) as extractive Question Answering to address this problem. |
| Outcome: | The proposed benchmark features 30,147 question-answer pairs derived from both Hebrew Wikipedia articles and Israeli tech news. |
Transformers are Multi-State RNNs (2024.emnlp-main)
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| Challenge: | Schwartz et al., 2017) have been using transformers for long-range tasks for NLP since the 1990s. |
| Approach: | They propose a transformer-only transformer with unlimited hidden state size that can be converted into bounded multistate RNNs by fixing the size of their hidden state. |
| Outcome: | The proposed compression policy outperforms baseline compression policies on long range tasks and LLMs. |
A Classifier of Word-Level Variants in Witnesses of Biblical Hebrew Manuscripts (2025.findings-acl)
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| Challenge: | a strong classifier (F1 value of 0.80) is trained to predict the category of difference between word pairs as present in collated (aligned) pairs of witnesses. |
| Approach: | The project is based on the relationship between available witnesses of biblical Hebrew and a strong classifier (F1 value of 0.80) is trained to predict the category of difference between word pairs as present in collated pairs of witnesses. |
| Outcome: | The proposed model is non-neural and uses part-of-speech tags, hand-crafted rules per category and synthetically derived data. |
Producing a Parallel Universal Dependencies Treebank of Ancient Hebrew and Ancient Greek via Cross-Lingual Projection (2024.lrec-main)
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| Challenge: | Using parallel treebanks, syntactic changes can be identified and evaluated in translations, redactions, and commentaries. |
| Approach: | They propose to construct a treebank of Ancient Greek containing portions of the Septuagint by word-aligning and projecting from the parallel Ancient Hebrew text. |
| Outcome: | The proposed treebank contains portions of the Hebrew Scriptures, which are translated into Ancient Greek, and is based on the results of a collaborative effort to create a crosslinguistically consistent treebank annotation scheme. |
Splintering Nonconcatenative Languages for Better Tokenization (2025.findings-acl)
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| Challenge: | Subword tokenizers are not effective in nonconcatenative languages such as Hebrew and Arabic, where morphology is encoded in root-template patterns, or Malay and Georgian, where split affixes are common. |
| Approach: | They propose a pre-processing step which rearranges text into a linear form that better represents nonconcatenative morphologies. |
| Outcome: | The proposed method is based on measures evaluating token vocabularies in Hebrew, Arabic, and Malay, and on BERT-architecture models trained for Hebrew. |