  Pre-trained transformer language models have shown remarkable performance on
a variety of NLP tasks. However, recent research has suggested that
phrase-level representations in these models reflect heavy influences of
lexical content, but lack evidence of sophisticated, compositional phrase
information. Here we investigate the impact of fine-tuning on the capacity of
contextualized embeddings to capture phrase meaning information beyond lexical
content. Specifically, we fine-tune models on an adversarial paraphrase
classification task with high lexical overlap, and on a sentiment
classification task. After fine-tuning, we analyze phrasal representations in
controlled settings following prior work. We find that fine-tuning largely
fails to benefit compositionality in these representations, though training on
sentiment yields a small, localized benefit for certain models. In follow-up
analyses, we identify confounding cues in the paraphrase dataset that may
explain the lack of composition benefits from that task, and we discuss
potential factors underlying the localized benefits from sentiment training.
