Probing Linguistic Features of Sentence-Level Representations in Relation Extraction

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.

Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals

“…4 To Probe or Not to Probe? By using the probing technique, different linguistic phenomenon such as POS, dependency information, and NER (Tenney et al, 2019a;Liu et al, 2019a;Alt et al, 2020) have been found to be ''easily extractable'' (typically using linear probes). A naive interpretation of these results may conclude that because information can be easily extracted by the probing model, this information is being used for the predictions.…”

A Feature Combination-Based Graph Convolutional Neural Network Model for Relation Extraction

“…The main neural networks used are the Convolutional Neural Network (CNN) [5][6][7] and Recurrent Neural Network (RNN) [8][9][10]. Moreover, according to whether the extracted entity pairs span sentences, the relation extraction model can be divided into a sentence-level relation extraction model [11] and cross-sentence relation extraction model [12]. There are also some researchers that perform document-level relation extraction [13,14].…”

An information theoretic view on selecting linguistic probes

“…Diagnostic probes were originally intended to explain information encoded in intermediate representations (Adi et al, 2017;Alain and Bengio, 2017;. Recently, various probing tasks have queried the representations of, e.g., contextualized word embeddings (Tenney et al, 2019a,b) and sentence embeddings (Linzen et al, 2016;Chen et al, 2019;Alt et al, 2020;Kassner and Schütze, 2020;Chi et al, 2020).…”