@inproceedings{majewska-etal-2021-verb,
title = "Verb Knowledge Injection for Multilingual Event Processing",
author = "Majewska, Olga and
Vuli{\'c}, Ivan and
Glava{\v{s}}, Goran and
Ponti, Edoardo Maria and
Korhonen, Anna",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.541",
doi = "10.18653/v1/2021.acl-long.541",
pages = "6952--6969",
abstract = "Linguistic probing of pretrained Transformer-based language models (LMs) revealed that they encode a range of syntactic and semantic properties of a language. However, they are still prone to fall back on superficial cues and simple heuristics to solve downstream tasks, rather than leverage deeper linguistic information. In this paper, we target a specific facet of linguistic knowledge, the interplay between verb meaning and argument structure. We investigate whether injecting explicit information on verbs{'} semantic-syntactic behaviour improves the performance of pretrained LMs in event extraction tasks, where accurate verb processing is paramount. Concretely, we impart the verb knowledge from curated lexical resources into dedicated adapter modules (verb adapters), allowing it to complement, in downstream tasks, the language knowledge obtained during LM-pretraining. We first demonstrate that injecting verb knowledge leads to performance gains in English event extraction. We then explore the utility of verb adapters for event extraction in other languages: we investigate 1) zero-shot language transfer with multilingual Transformers and 2) transfer via (noisy automatic) translation of English verb-based lexical knowledge. Our results show that the benefits of verb knowledge injection indeed extend to other languages, even when relying on noisily translated lexical knowledge.",
}
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<abstract>Linguistic probing of pretrained Transformer-based language models (LMs) revealed that they encode a range of syntactic and semantic properties of a language. However, they are still prone to fall back on superficial cues and simple heuristics to solve downstream tasks, rather than leverage deeper linguistic information. In this paper, we target a specific facet of linguistic knowledge, the interplay between verb meaning and argument structure. We investigate whether injecting explicit information on verbs’ semantic-syntactic behaviour improves the performance of pretrained LMs in event extraction tasks, where accurate verb processing is paramount. Concretely, we impart the verb knowledge from curated lexical resources into dedicated adapter modules (verb adapters), allowing it to complement, in downstream tasks, the language knowledge obtained during LM-pretraining. We first demonstrate that injecting verb knowledge leads to performance gains in English event extraction. We then explore the utility of verb adapters for event extraction in other languages: we investigate 1) zero-shot language transfer with multilingual Transformers and 2) transfer via (noisy automatic) translation of English verb-based lexical knowledge. Our results show that the benefits of verb knowledge injection indeed extend to other languages, even when relying on noisily translated lexical knowledge.</abstract>
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%0 Conference Proceedings
%T Verb Knowledge Injection for Multilingual Event Processing
%A Majewska, Olga
%A Vulić, Ivan
%A Glavaš, Goran
%A Ponti, Edoardo Maria
%A Korhonen, Anna
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F majewska-etal-2021-verb
%X Linguistic probing of pretrained Transformer-based language models (LMs) revealed that they encode a range of syntactic and semantic properties of a language. However, they are still prone to fall back on superficial cues and simple heuristics to solve downstream tasks, rather than leverage deeper linguistic information. In this paper, we target a specific facet of linguistic knowledge, the interplay between verb meaning and argument structure. We investigate whether injecting explicit information on verbs’ semantic-syntactic behaviour improves the performance of pretrained LMs in event extraction tasks, where accurate verb processing is paramount. Concretely, we impart the verb knowledge from curated lexical resources into dedicated adapter modules (verb adapters), allowing it to complement, in downstream tasks, the language knowledge obtained during LM-pretraining. We first demonstrate that injecting verb knowledge leads to performance gains in English event extraction. We then explore the utility of verb adapters for event extraction in other languages: we investigate 1) zero-shot language transfer with multilingual Transformers and 2) transfer via (noisy automatic) translation of English verb-based lexical knowledge. Our results show that the benefits of verb knowledge injection indeed extend to other languages, even when relying on noisily translated lexical knowledge.
%R 10.18653/v1/2021.acl-long.541
%U https://aclanthology.org/2021.acl-long.541
%U https://doi.org/10.18653/v1/2021.acl-long.541
%P 6952-6969
Markdown (Informal)
[Verb Knowledge Injection for Multilingual Event Processing](https://aclanthology.org/2021.acl-long.541) (Majewska et al., ACL-IJCNLP 2021)
ACL
- Olga Majewska, Ivan Vulić, Goran Glavaš, Edoardo Maria Ponti, and Anna Korhonen. 2021. Verb Knowledge Injection for Multilingual Event Processing. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 6952–6969, Online. Association for Computational Linguistics.