In this article, we present how to improve AllenNLP’s coreference resolution model in order to achieve a more coherent output. We also introduce a couple of ensemble strategies to make use of both Huggingface and AllenNLP models at the same time.

In short, coreference resolution (CR) is an NLP task that aims to replace all ambiguous words in a sentence so that we get a text that doesn’t need any extra context to be understood. If you need a refresher on some basic concepts, refer to  our introductory article.

Here, we focus mainly on improving how the libraries resolve found clusters. If you are interested in a detailed  explanation of the most common libraries for CR, and our motivations feel free to check it out.

Ready-to-use yet incomplete

Both Huggingface and AllenNLP coreference resolution models would be a great addition to many projects. However, we’ve found several drawbacks (described in detail in the previous article) that made us doubt whether we truly wanted to implement those libraries in our system. The most substantial problem isn’t the inability to find acceptable clusters but the last step of the whole process – resolving coreferences in order to obtain an unambiguous text.

#nlp #artificial-intelligence #huggingface #github