| Challenge: | Existing semantic role annotation resources are lacking for German. |
| Approach: | They propose a translation-based approach to train German semantic role models using semantic annotations and alignment models. |
| Outcome: | The proposed method achieves competitive evaluation scores, but avoids limitations of previous approaches. |
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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. |
Using Semantic Role Labeling to Improve Neural Machine Translation (2022.lrec-1)
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| Challenge: | despite progress in machine translation, some form of language understanding may be desirable . current systems rely on pattern recognition, but some form may be useful . |
| Approach: | They use semantic role labeling to annotate a standard parallel corpus with semantic roles . they then train a neural machine translation system using the annotated corpus and original unannotated text . |
| Outcome: | The proposed system improves BLEU scores for English, French, German, Greek and Spanish. |
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. |
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 . |
Annotation and Automatic Classification of Aspectual Categories (P19-1)
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| Challenge: | Annotated resource for aspectual classification of German verb tokens in context. |
| Approach: | They present a resource for aspectual classification of German verb tokens in their clausal context. |
| Outcome: | The proposed resource is compared with previous work on German verb tokens using aspectual features compatible with the plurality of aspectual classifications. |
Semantic Role Labeling as Syntactic Dependency Parsing (2020.emnlp-main)
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| Challenge: | Using propBank-style semantic role labeling, we reduce the task to syntactic dependency parsing. |
| Approach: | They propose to convert SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format. |
| Outcome: | The proposed scheme reduces the task of (span-based) PropBank-style semantic role labeling to syntactic dependency parsing. |
Alignment-free Cross-lingual Semantic Role Labeling (2020.emnlp-main)
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| 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. |
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. |
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. |
Syntax-driven Approach for Semantic Role Labeling (2022.lrec-1)
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| Challenge: | Existing studies focus on auto-generated syntactic knowledge to enhance semantic role labeling . experimental results show that map memories can enhance SRL . |
| Approach: | They propose to map memories to enhance semantic role labeling by encoding auto-generated syntactic knowledge from off-the-shelf toolkits. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art results on two English benchmark datasets. |