A Cross-Task Analysis of Text Span Representations
This work addresses the need for effective span representations in NLP, but it is incremental as it focuses on empirical analysis rather than introducing new methods.
The paper tackled the problem of representing arbitrary text spans in NLP tasks by empirically evaluating six span representation methods across six tasks, finding that the optimal span representation varies by task and has a bigger impact with fixed pretrained encoders than fine-tuned ones.
Many natural language processing (NLP) tasks involve reasoning with textual spans, including question answering, entity recognition, and coreference resolution. While extensive research has focused on functional architectures for representing words and sentences, there is less work on representing arbitrary spans of text within sentences. In this paper, we conduct a comprehensive empirical evaluation of six span representation methods using eight pretrained language representation models across six tasks, including two tasks that we introduce. We find that, although some simple span representations are fairly reliable across tasks, in general the optimal span representation varies by task, and can also vary within different facets of individual tasks. We also find that the choice of span representation has a bigger impact with a fixed pretrained encoder than with a fine-tuned encoder.