WHAT DO YOU LEARN FROM CONTEXT? PROBING FOR SENTENCE STRUCTURE IN CONTEXTUALIZED WORD REPRESENTATIONS
Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline.
This is a nice paper that attempts to tease apart some questions about the effectiveness of contextual word embeddings (ELMo, CoVe, and the Transformer LM). The main question is about the value of context in these representations, and in particular how their ability to encode context allows them to also (implicitly) represent linguistic properties of words. What I really like about the paper is the “Edge probing” method it introduces. The idea is to probe the representations using diagnostic classifiers—something that’s already widespread practice—but to focus on the relationship between spans rather than individual words. This is really nice because it enables them to look at more than just tagging problems: the paper looks at syntactic constituency, dependencies, entity labels, and semantic role labeling. I think the combination of an interesting research question and a new method (which will probably be picked up by others working in this area) make this a strong candidate for ICLR. The paper is well-written and experimentally thorough.
Nitpick: It would be nice to see some examples of cases where the edge probe is correct, and where it isn’t.