| Challenge: | Using parameter sharing between parsers of related languages can improve performance, but there is no consensus on what parameters to share. |
| Approach: | They propose a model where transition classifier parameters are shared and word and character parameters are controlled by a parameter that can be tuned on validation data. |
| Outcome: | The proposed model improves on a monolingually trained baseline. |
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Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine Translation (2023.emnlp-main)
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| Challenge: | Using a shared vocabulary is common practice in multilingual machine translation . however, when words overlap is small, e.g., using different writing systems, knowledge transfer is inhibited . |
| Approach: | They propose a re-parameterized method for building word embeddings using word equivalence classes and graph networks to fuse word embeds across languages. |
| Outcome: | The proposed method achieves evident BLEU improvements on high- and low-resource MNMT scenarios. |
Boosting Inference Efficiency: Unleashing the Power of Parameter-Shared Pre-trained Language Models (2023.findings-emnlp)
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| Challenge: | Parameter-shared pre-trained language models (PLMs) have emerged as a successful approach in resource-constrained environments. |
| Approach: | They propose a method to enhance the inference efficiency of parameter-shared PLMs by pre-training models that can achieve even greater acceleration. |
| Outcome: | The proposed method improves inference efficiency on autoregressive and autoencoding models. |
Parser Training with Heterogeneous Treebanks (P18-2)
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| Challenge: | In the 2017 CoNLL Shared Task on Universal Dependency Parsing, 25 languages have more than one treebank . many teams did not take advantage of the multiple treebanks, however, and trained one model per treebank instead of one model for each language. |
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Cheating a Parser to Death: Data-driven Cross-Treebank Annotation Transfer (L18-1)
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| Challenge: | Using annotated corpus for linguistic purposes is no longer justified . hand-crafted syntactic resources such as grammars and lexicons can be used as sources of features to guide data driven systems. |
| Approach: | They propose an efficient method for transferring annotations between two different treebanks of the same language. |
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Shared-Private Bilingual Word Embeddings for Neural Machine Translation (P19-1)
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| Challenge: | Word embedding is central to neural machine translation, but indirectly interfaces with other layers, making them comparatively isolated. |
| Approach: | They propose a shared-private bilingual word embedding which gives a closer relationship between the source and target embedders and reduces the number of model parameters. |
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Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers (D19-1)
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| Challenge: | linguistic typology has shown great promise in pre-neural parsing, but results for neural architectures have been mixed. |
| Approach: | They explore the task of leveraging typology in the context of cross-lingual dependency parsing. |
| Outcome: | The proposed approach improves performance in the context of cross-lingual dependency parsing. |
SharVeT: Similarity-aware Parameter Sharing with Vector-based Tuning for Efficient LLM Compression (2026.acl-long)
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| Challenge: | Existing methods for parameter sharing rely on naive grouping and fail to correct sharing-induced discrepancies. |
| Approach: | They propose a parameter sharing framework that performs similarity-based grouping to ensure accurate sharing and allocates parameters adaptively to preserve diversity within each group. |
| Outcome: | The proposed framework outperforms existing methods, achieving 32.1% lower perplexity and 23.3% higher few-shot reasoning accuracy. |
UDapter: Language Adaptation for Truly Universal Dependency Parsing (2020.emnlp-main)
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| Challenge: | Cross-language interference and restrained model capacity remain major obstacles in multilingual dependency parsing. |
| Approach: | They propose a multilingual task adaptation approach based on contextual parameter generation and adapter modules that learn adapters via language embeddings while sharing model parameters across languages. |
| Outcome: | The proposed approach outperforms strong monolingual and multilingual baselines on most languages on high-resource and low-resourced (zero-shot) languages. |
Transition-based Parsing with Stack-Transformers (2020.findings-emnlp)
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| Challenge: | Existing parsing systems use local or global models of the parser state to improve performance. |
| Approach: | They propose to modify the sequence-to-sequence Transformer to model global or local parser states in transition-based parsing. |
| Outcome: | The proposed model significantly improves performance on dependency and Abstract Meaning Representation (AMR) parsing tasks. |
Quantifying training challenges of dependency parsers (C18-1)
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| Challenge: | a new metric is introduced to evaluate the difficulty to learn a given class of dependencies . a series of systematic computations using that metric have revealed interesting properties of the 3 considered parsing algorithms . |
| Approach: | They introduce a new metric to evaluate the difficulty to learn a given class of dependencies . they use it to characterize the information conveyed by cross-lingual parsers . |
| Outcome: | The proposed metric reveals the kind of dependencies that require high effort during training . it also shows that cross-lingual parsers can provide better quality information . |