[Feature] Add Ner Suffix feature

[XINGXIAOYU]

Description

Add a parameter "tagging_first_token", so you can choose to use the first piece or the last piece of each word. The first piece catches the prefix feature of a word, and the last piece catches the suffix feature of a word.

Checklist

Essentials

• PR's title starts with a category (e.g. [BUGFIX], [MODEL], [TUTORIAL], [FEATURE], [DOC], etc)
• Changes are complete (i.e. I finished coding on this PR)
• All changes have test coverage
• Code is well-documented

Comments

• If this change is a backward incompatible change, why must this change be made.
• Interesting edge cases to note here

[codecov]

Codecov Report

Merging #1123 into master will decrease coverage by 0.12%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master    #1123      +/-   ##
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- Coverage   88.34%   88.21%   -0.13%     
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  Files          66       66              
  Lines        6290     6290              
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- Hits         5557     5549       -8     
- Misses        733      741       +8

Impacted Files	Coverage Δ
src/gluonnlp/model/bert.py	88.06% <0%> (-4.12%)	⬇️
src/gluonnlp/utils/files.py	45.9% <0%> (+3.27%)	⬆️

[mli]

Job PR-1123/1 is complete.
Docs are uploaded to http://gluon-nlp-staging.s3-accelerate.dualstack.amazonaws.com/PR-1123/1/index.html

[leezu]

How about parser.add_argument('--tag-last-token', action='store_true'). It seems simpler to call finetune_bert.py --tag-last-token than finetune_bert.py --tagging-first-token=False.

In either case please update the test case in scripts/tests/ to run invoke the finetune_bert.py with both options. You can parametrize the test following for example Haibin's recent PR: https://github.com/dmlc/gluon-nlp/pull/1121/files#diff-fa82d34d543ff657c2fe09553bd0fa34R234

[XINGXIAOYU]

Sure, I will update it.

[sxjscience]

Have you found any performance differences?

[XINGXIAOYU]

@sxjscience I've tried the default parameters set in the scripts on conll2003 dataset. The performance using suffix feature will be a little lower than using the prefix feature.

[sxjscience]

I think we can try the following:

• use the state corresponds to the first subword token
• use the state corresponds to the last subword token
• use the average pooling on top of the states of all the subwords
• use the max pooling on top of the states of all the subwords

[sxjscience]

One problem is that since we are using self-attention, we are able to tailor the attention weights to cover the first, last, average cases. Thus, I don't think selecting the first/last token will impact the performance much.

[XINGXIAOYU]

@sxjscience In classification task, I think it does not matter. But in sequence labeling task, one word has one label. If we break the word 'w' into several subwords [sw1,sw2,...], then only sw1 will have the label, and the labels of the others will set to NULL. I think it does not make sense.

[liuzh47]

Pls add white space after the comma.

[sxjscience]

Due to the fact that we are using attention, the state bound to sw1 will be related to the other sub-words. The same thing happens for sw_n. A reasonable approach is to mask the loss corresponding to the other sub-word tokens and only use the state of the first subword as the contextualized word embedding. Get Outlook for iOS<https://aka.ms/o0ukef>

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________________________________ From: Shawnyu <notifications@github.com> Sent: Sunday, January 19, 2020 7:28:28 PM To: dmlc/gluon-nlp <gluon-nlp@noreply.github.com> Cc: Xingjian SHI <xshiab@connect.ust.hk>; Mention <mention@noreply.github.com> Subject: Re: [dmlc/gluon-nlp] [Feature] Add Ner Suffix feature (#1123) @sxjscience<https://github.com/sxjscience> In classification task, I think it does not matter. But in sequence labeling task, one word has one label. If we break the word 'w' into several subwords [sw1,sw2,...], then only sw1 will have the label, and the labels of the others will set to NULL. I think it does not make sense. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub<#1123?email_source=notifications&email_token=ABHQH3RQMOA4X4WSVHE6VADQ6UK5ZA5CNFSM4KIXYPMKYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOEJLHMDY#issuecomment-576091663>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/ABHQH3UUE5HVOS5AHRINZLLQ6UK5ZANCNFSM4KIXYPMA>.

[liuzh47]

Can both cases be merged here? For example, if len(tmptext) == 0, you can still have text = tmptext + token_text which is equivalent to token_text.

[liuzh47]

better name it as tmp_text. Or what about partial_text?

[XINGXIAOYU]

@sxjscience Agree with you, and I'll try this method.

[liuzh47]

A reasonable approach is to mask the loss corresponding to the other sub-word tokens and only use the state of the first subword as the contextualized word embedding.

I am confused about this part. Why masking loss of other sub-word tokens is reasonable? For example on NER tasks, suffix is much more important than prefix in words like firefighter.

[sxjscience]

A reasonable approach is to mask the loss corresponding to the other sub-word tokens and only use the state of the first subword as the contextualized word embedding.

I am confused about this part. Why masking loss of other sub-word tokens is reasonable? For example on NER tasks, suffix is much more important than prefix of the words like firefighter.

Since we are using attention, the higher-level state associated with fire has already captured the information about fighter. Suffix should be more appropriate if we are using a LSTM. However, we are using attention and we are actually attending to the whole input.

[chenw23]

@sxjscience Do you think we should continue with this pull request?
If we should, maybe I can take over the remaining work that needs to be done on this pull request.