NER performance with Ontonotes and number-related ELMo embeddings about bilm-tf HOT 5 CLOSED

Thanks for posting this with the excellent details! This is quite interesting. I haven't noticed any strange effects with numbers, but haven't looked at them in detail. We aren't doing any special tokenization or normalization of numbers when training the model, and they are treated the same as all other tokens. When using datasets like Ontonotes we also just use the existing, provided tokenization.

Two questions:

• can you post an example of a few sentences from the dataset with these entity classes (e.g. ORDINAL, QUANTITY, DATE, etc)? This might help uncover the problem.
• have you tried the original ELMo model (not the 5.5B model) for this task? We haven't experimented with the 5.5B model too heavily, and it's possible this is a problem with the 5.5B model but not in the original model.

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Hi @kermitt2 -- just to follow up, we aren't able to reproduce these results on our end, and we are seeing improved performance with ELMo for all entity types in this dataset (including ORDINAL, etc). Perhaps it's something particular to how you are handling numbers vs strings in your pre-processing pipeline?

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Hello @matt-peters
Sorry for the late reply. The follow-up is super useful and I will revisit and double check my pre-processing given that it is thus coming from my side. The original ELMo model gave similar results for me. Many thanks!

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Just to close the loop on this, we saw a 0.882 development set F1 using the 5.5B ELMo model for this dataset (haven't checked the test set performance but it should be similar).

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Hey can you tell me where i can download the pretrained elmo ner ontonotes model. Thanks

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