Towards Semi-Automatic Generation of Proposition Banks for
            Low-Resource Languages

Akbik, Alan; Kumar, Vishwajeet; Li, Yunyao

doi:10.18653/v1/d16-1102

Cited by 14 publications

(11 citation statements)

References 9 publications

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“…Instead of dealing with heterogeneous linguistic theories, another line of research consists in actively studying the effect of using a single formalism across multiple languages through annotation projection or other transfer techniques (Akbik et al, 2015(Akbik et al, , 2016Daza and Frank, 2019;Cai and Lapata, 2020;Daza and Frank, 2020). However, such approaches often rely on word aligners and/or automatic translation tools which may introduce a considerable amount of noise, especially in lowresource languages.…”

Section: Introductionmentioning

confidence: 99%

Unifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources

Conia¹,

Bacciu²,

Navigli³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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While cross-lingual techniques are finding increasing success in a wide range of Natural Language Processing tasks, their application to Semantic Role Labeling (SRL) has been strongly limited by the fact that each language adopts its own linguistic formalism, from Prop-Bank for English to AnCora for Spanish and PDT-Vallex for Czech, inter alia. In this work, we address this issue and present a unified model to perform cross-lingual SRL over heterogeneous linguistic resources. Our model implicitly learns a high-quality mapping for different formalisms across diverse languages without resorting to word alignment and/or translation techniques. We find that, not only is our cross-lingual system competitive with the current state of the art but that it is also robust to low-data scenarios. Most interestingly, our unified 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. We release our code and model at https://github.com/ SapienzaNLP/unify-srl.

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Section: Introductionmentioning

confidence: 99%

Unifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources

Conia¹,

Bacciu²,

Navigli³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…Symbolic SRT schemes such as SRL schemes and AMR have also been studied for their crosslinguistic applicability (Padó and Lapata, 2009;Sun et al, 2010;Xue et al, 2014), indicating partial portability across languages. Translated versions of PropBank and FrameNet have been constructed for multiple languages (e.g., Akbik et al, 2016;Hartmann and Gurevych, 2013). How-ever, as both PropBank and FrameNet are lexicalized schemes, and as lexicons diverge wildly across languages, these schemes require considerable adaptation when ported across languages (Kozhevnikov and Titov, 2013).…”

Section: Discussionmentioning

confidence: 99%

The State of the Art in Semantic Representation

Abend¹,

Rappoport²

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

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Semantic representation is receiving growing attention in NLP in the past few years, and many proposals for semantic schemes (e.g., AMR, UCCA, GMB, UDS) have been put forth. Yet, little has been done to assess the achievements and the shortcomings of these new contenders, compare them with syntactic schemes, and clarify the general goals of research on semantic representation. We address these gaps by critically surveying the state of the art in the field.

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“…Creating SRL datasets requires expert annotation, which is expensive. While there are some efforts on semi-automatic annotation targeting low-resource languages (e.g., Akbik et al, 2016), achieving high neural network performance with small or unlabeled datasets remains a challenge (e.g., Lapata, 2009, 2012;Titov and Klementiev, 2012;Gormley et al, 2014;Abend et al, 2009).…”

Section: Scenario 1: Low Training Datamentioning

confidence: 99%

Structured Tuning for Semantic Role Labeling

Li¹,

Jawale²,

Palmer³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Recent neural network-driven semantic role labeling (SRL) systems have shown impressive improvements in F1 scores. These improvements are due to expressive input representations, which, at least at the surface, are orthogonal to knowledge-rich constrained decoding mechanisms that helped linear SRL models. Introducing the benefits of structure to inform neural models presents a methodological challenge. In this paper, we present a structured tuning framework to improve models using softened constraints only at training time. Our framework leverages the expressiveness of neural networks and provides supervision with structured loss components. We start with a strong baseline (RoBERTa) to validate the impact of our approach, and show that our framework outperforms the baseline by learning to comply with declarative constraints. Additionally, our experiments with smaller training sizes show that we can achieve consistent improvements under low-resource scenarios. BERT CoNLL-2012 (100%) +U,F,O 0.5 1 0.1Table 10: Values of hyperparameter λ's.

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Towards Semi-Automatic Generation of Proposition Banks for Low-Resource Languages

Cited by 14 publications

References 9 publications

Unifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources

Unifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources

The State of the Art in Semantic Representation

Structured Tuning for Semantic Role Labeling

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