Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction
This work addresses the interpretability gap in neural relation extraction models for researchers and practitioners, providing insights into how architectural choices and linguistic features affect learned representations, though it is incremental in nature.
The authors investigated what linguistic features are captured by state-of-the-art neural relation extraction models by introducing 14 probing tasks and testing over 40 encoder and feature combinations on two datasets. They found that adding contextualized word representations boosts performance on tasks involving named entities and part-of-speech, improving relation extraction, while entity masking helps relation extraction but reduces performance on entity-related probing tasks.
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.