Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction

Christoph Alt, Aleksandra Gabryszak, Leonhard Hennig


Abstract
Despite the recent progress, little is known about the features captured by state-of-the-art neural relation extraction (RE) models. Common methods encode the source sentence, conditioned on the entity mentions, before classifying the relation. However, the complexity of the task makes it difficult to understand how encoder architecture and supporting linguistic knowledge affect the features learned by the encoder. We introduce 14 probing tasks targeting linguistic properties relevant to RE, and we use them to study representations learned by more than 40 different encoder architecture and linguistic feature combinations trained on two datasets, TACRED and SemEval 2010 Task 8. We find that the bias induced by the architecture and the inclusion of linguistic features are clearly expressed in the probing task performance. For example, adding contextualized word representations greatly increases performance on probing tasks with a focus on named entity and part-of-speech information, and yields better results in RE. In contrast, entity masking improves RE, but considerably lowers performance on entity type related probing tasks.
Anthology ID:
2020.acl-main.140
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1534–1545
Language:
URL:
https://aclanthology.org/2020.acl-main.140
DOI:
10.18653/v1/2020.acl-main.140
Bibkey:
Cite (ACL):
Christoph Alt, Aleksandra Gabryszak, and Leonhard Hennig. 2020. Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1534–1545, Online. Association for Computational Linguistics.
Cite (Informal):
Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction (Alt et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.140.pdf
Video:
 http://slideslive.com/38929091
Code
 DFKI-NLP/RelEx +  additional community code
Data
SemEval-2010 Task 8