| 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. |
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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. |
What’s Wrong with Hebrew NLP? And How to Make it Right (D19-3)
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| Challenge: | Sub-optimal performance of many morphologically rich languages (MRLs) is due to errors in early morphology disambiguation decisions, that cannot be recovered later on in the pipeline, yielding incoherent annotations on the whole. |
| Approach: | They propose to use a joint morpho-syntactic infrastructure for processing Modern Hebrew texts to provide rich and expressive annotations. |
| Outcome: | The proposed pipelines are based on a morpho-syntactic infrastructure for processing Modern Hebrew texts. |
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. |
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 . |
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. |
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. |
One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks (2024.findings-emnlp)
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| Challenge: | Morphologically rich languages are notoriously challenging to process for downstream NLP applications. |
| Approach: | They propose a pretrained model for NLP applications involving the morphologically rich language Sanskrit that outperforms previous models by a considerable margin. |
| Outcome: | The proposed model outperforms tokenized models on established Sanskrit word segmentation tasks and matches the current best lexicon-based model. |
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. |
A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages (2021.eacl-srw)
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| Challenge: | Neural dependency parsing has been a success for many domains and languages, but the bottleneck of massive labelled data limits its effectiveness for low resource languages. |
| Approach: | They propose to use morphological knowledge to improve dependency parsing for morphology rich languages in a low-resource setting to perform experiments. |
| Outcome: | The proposed method achieves an average gain of 2 points (UAS) and 3.6 points (LAS) on 10 MRLs in low-resource settings. |
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. |