| Challenge: | Existing approaches to semantic role labeling rely on word alignments, translation engines or preprocessing tools. |
| Approach: | They propose a cross-lingual semantic role labeling model which only requires annotations in a source language and access to raw text in . |
| Outcome: | The proposed model minimizes the effort required to construct annotations or models for a new target language. |
Similar Papers
Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus (2020.acl-main)
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| Challenge: | Existing approaches to semantic role labeling (SRL) are focusing on the English language. |
| Approach: | They propose a method for semantic role labeling that uses corpus translation to build training datasets from SRL annotations. |
| Outcome: | The proposed method is highly effective and can improve the target-language performance significantly. |
Translate and Label! An Encoder-Decoder Approach for Cross-lingual Semantic Role Labeling (D19-1)
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| Challenge: | Unlike annotation projection techniques, our model does not need parallel data during inference time. |
| Approach: | They propose a cross-lingual Encoder-Decoder model that simultaneously translates and generates sentences with semantic role annotations in a resource-poor target language. |
| Outcome: | The proposed model can be applied in monolingual, multilingual and cross-lingual settings and produces dependency-based and span-based annotations. |
UniteD-SRL: A Unified Dataset for Span- and Dependency-Based Multilingual and Cross-Lingual Semantic Role Labeling (2021.findings-emnlp)
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| Challenge: | Multilingual and cross-lingual Semantic Role Labeling (SRL) has attracted increasing attention as multilingual text representation techniques have become more effective and widely available. |
| Approach: | They propose a benchmark for multilingual and cross-lingual, span- and dependency-based SRL that provides expert-curated parallel annotations using a common predicate-argument structure inventory. |
| Outcome: | The proposed benchmark provides expert-curated parallel annotations using a common predicate-argument structure inventory, allowing direct comparisons across languages and encouraging studies on cross-lingual transfer in SRL. |
Unifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources (2021.naacl-main)
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| Challenge: | Using cross-lingual techniques to perform Semantic Role Labeling (SRL) has been limited by the fact that each language adopts its own linguistic formalism . |
| Approach: | They propose a unified model to perform cross-lingual SRL over heterogeneous linguistic resources. |
| Outcome: | The proposed model is able to annotate a sentence in a single forward pass with all the inventories it was trained with, providing a tool for the analysis and comparison of linguistic theories across different languages. |
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. |
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. |
Syntax-aware Multilingual Semantic Role Labeling (D19-1)
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| Challenge: | Existing work on semantic role labeling (SRL) on English has focused on syntactic integration and enhanced word representation. |
| Approach: | They propose a method guided by syntactic rule to prune arguments to integrate syntax into multilingual SRL model simply and effectively. |
| Outcome: | The proposed model achieves state-of-the-art results on the CoNLL-2009 benchmarks of all seven languages. |
On the Benefit of Syntactic Supervision for Cross-lingual Transfer in Semantic Role Labeling (2021.emnlp-main)
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| Challenge: | Recent advances in neural architectures and pre-trained representations have greatly improved the performance of fully-supervised semantic role labeling (SRL) but there are limitations in the availability of supervised training data. |
| Approach: | They propose to leverage syntactic dependencies to facilitate cross-lingual transfer by annotating predicate-argument structures in text. |
| Outcome: | The proposed model can be extended to other languages with limited training data. |
Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach (2020.coling-main)
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| Challenge: | Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling. |
| Approach: | They propose a language-agnostic model that does away with morphological and syntactic features to achieve robustness across languages. |
| Outcome: | The proposed model outperforms the state-of-the-art in all languages of the CoNLL-2009 benchmark dataset. |
CLAR: A Cross-Lingual Argument Regularizer for Semantic Role Labeling (2020.findings-emnlp)
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| Challenge: | Existing methods for training one model on multiple languages outperform monolingual baselines for low resource languages. |
| Approach: | They propose a method to combine training data from multiple languages to create a shared representation space for the model. |
| Outcome: | The proposed method outperforms monolingual and polyglot training on low resource languages. |