Generating High Quality Proposition Banks for Multilingual Semantic Role Labeling
By Alan Akbik, Laura Chiticariu, Marina Danilevsky, Yunyao Li, Shivakumar Vaithyanathan, Huaiyu Zhu. 2015
101 citations recorded by the source corpus.
Semantic role labeling (SRL) is crucial to natural language understanding as it identifies the predicate-argument structure in text with semantic labels. Unfortunately, resources required to construct SRL models are expensive to obtain and simply do not exist for most languages. In this paper, we present a two-stage method to enable the construction of SRL models for resourcepoor languages by exploiting monolingual SRL and multilingual parallel data. Experimental results show that our method outperforms existing methods. We use our method to generate Proposition Banks with high to reasonable quality for 7 languages in three language families and release these resources to the research community.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sebastian Riedel
- Patrick Lewis
- Luke S. Zettlemoyer
- Barlas Oğuz
- Graham Neubig
- Mohit Bansal
- Wen-tau Yih
- Hannaneh Hajishirzi
In Natural Language Processing
- Sebastian Riedel
- Patrick Lewis
- Barlas Oğuz
- Ruty Rinott
- Sebastian Ruder
- Luke S. Zettlemoyer
- Graham Neubig
- Holger Schwenk
Citations in the corpus
- MLQA: Evaluating Cross-lingual Extractive Question Answering (2019, background): “…in cross-lingual tasks such as document classification (Lewis et al., 2004; Klementiev et al., 2012; Schwenk and Li, 2018), semantic role labelling (Akbik et al., 2015) and NLI (Conneau et al., 2018), we argue that while multilingual QA training data might be useful but not strictly necessary,…”