neulab / compare-mt Goto Github PK

Would it be nice to implement ROUGE as it is often used in tasks like text summarization?

If the same sentence appears multiple times in the test set, it will appear multiple times in the sentence report. It would be nice to de-duplicate these.

Currently compare_mt.py only works with exactly two systems. It'd be nice if it was possible to specify more than two systems.

For some things this is relatively easy. For example, aggregate score analysis, word accuracy analysis, sentence bucket analysis are all calculated with respect to single systems, so this should be easy.

For other things, like n-gram analysis, and sentence difference analysis, this is harder because the analysis is pairwise. In this case, we might just have to specify which systems to compare.

It'd be good to give systems different names via command line arguments.

Hello,

I was checking your notes (http://www.phontron.com/class/mtandseq2seq2018/assets/slides/mt-fall2018.chapter11.pdf) and saw the following, which seems to be applied in this codebase as well:

In Line 4, we sample a subset of the test data, where in practice we usually use exactly half of the sentences in the test data.

If I understand correctly, if we have n sentences in the test set, this means that every bootstrap resample has only 0.5 * n sentences in it. What is the intuition of using half of the sentences here?

Thanks

Currently the script does not allow analysis over source words.

It would be nice if we could use alignments to measure the f-measure of source words/n-grams according to various criteria. One example of this is the analysis done by @frederick0329 in the paper Handling Homographs in Neural Machine Translation

Right now the threshold for statistical significance is 0.05, would be useful to be able to specify a different value in the command line.

flake8 testing of https://github.com/neulab/compare-mt on Python 3.7.1

$ flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics

./compare_mt/compare_mt_main.py:391:60: F821 undefined name 'sys_names'
    raise ValueError(f'len(sys_names) != len(outs) -- {len(sys_names)} != {len(outs)}')
                                                           ^
1     F821 undefined name 'sys_names'
1

E901,E999,F821,F822,F823 are the "showstopper" flake8 issues that can halt the runtime with a SyntaxError, NameError, etc. These 5 are different from most other flake8 issues which are merely "style violations" -- useful for readability but they do not effect runtime safety.

• F821: undefined name name
• F822: undefined name name in __all__
• F823: local variable name referenced before assignment
• E901: SyntaxError or IndentationError
• E999: SyntaxError -- failed to compile a file into an Abstract Syntax Tree

When installing compare-mt with pip, usually get following errors:

Something automatically deployed with travis CI.

According to my understanding (and the original paper: https://www.aclweb.org/anthology/W04-3250), bootstrap resampling should be performed with replacement.

However If I understand the current code we are sampling without replacement. This is probably not going to change actual results that much but we should still fix it for the sake of consistency (for instance one instance where sampling without replacement goes wring is that the more you increase the number of samples the less accurate the confidence intervals become).

EDIT: I think those are the lines where we are sampling: https://github.com/neulab/compare-mt/blob/master/compare_mt/sign_utils.py#L47-L48

Instead we should do

reduced_ids = np.random.choice(ids, size=int(len(ids)*sample_ratio), replace=True)

For example:

$ pip install compare_mt
Collecting compare_mt
  Downloading https://files.pythonhosted.org/packages/e9/4c/e18ea230d656e273a1dd8a0fcea74154ebb343ea471dce7e95a6cd74ed7d/compare_mt-0.2.8.tar.gz (43kB)
     |████████████████████████████████| 51kB 2.2MB/s 
    ERROR: Command errored out with exit status 1:
     command: /home/gneubig/anaconda3/envs/python3/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-sieojamx/compare-mt/setup.py'"'"'; __file__='"'"'/tmp/pip-install-sieojamx/compare-mt/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /tmp/pip-install-sieojamx/compare-mt/pip-egg-info
         cwd: /tmp/pip-install-sieojamx/compare-mt/
    Complete output (9 lines):
    Traceback (most recent call last):
      File "<string>", line 1, in <module>
      File "/tmp/pip-install-sieojamx/compare-mt/setup.py", line 4, in <module>
        import compare_mt
      File "/tmp/pip-install-sieojamx/compare-mt/compare_mt/__init__.py", line 5, in <module>
        import compare_mt.scorers
      File "/tmp/pip-install-sieojamx/compare-mt/compare_mt/scorers.py", line 1, in <module>
        import nltk
    ModuleNotFoundError: No module named 'nltk'
    ----------------------------------------
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.

compare-mt implements many scorers, it might be nice to be able to use it to just implement a stand-alone scorer for text files that gives you access to any of the scorers provided by compare-mt.

Implementation of Word Error Rate would be nice to have for applications like speech. Here is an implementation that I have, it'd need to be copy-pasted into a scorer.

def edits(src, trg, sub_pen=1.0, ins_pen=1.0, del_pen=1.0):
  # Types:
  # 0 is equal
  # 1 is sub
  # 2 is delete
  # 3 is insert
  sp1 = len(src)+1
  tp1 = len(trg)+1
  scores = np.zeros((sp1, tp1))
  actions = np.zeros((sp1, tp1), np.int32)
  equals = np.expand_dims(np.array(src), axis=1) == np.array(trg)
  scores[:,0] = range(sp1)
  actions[1:,0] = 2
  scores[0,:] = range(tp1)
  actions[0,1:] = 3
  # Forward edit distance
  for i in range(0, len(src)):
    for j in range(0, len(trg)):
      my_action = 0 if equals[i,j] else 1
      my_score = scores[i,j] + my_action * sub_pen
      del_score = scores[i,j+1] + del_pen 
      if del_score < my_score:
        my_action = 2
        my_score = del_score
      ins_score = scores[i+1,j] + ins_pen 
      if ins_score < my_score:
        my_action = 3
        my_score = ins_score
      actions[i+1,j+1] = my_action
      scores[i+1,j+1] = my_score
  # Backward edits
  i, j = len(src), len(trg)
  best_actions = []
  while i+j != 0:
    my_action = actions[i,j]
    best_actions.append(my_action)
    if my_action != 2: j -= 1
    if my_action != 3: i -= 1
  best_actions.reverse() 
  return scores[-1,-1], best_actions

Now that we have our own implementation of BLEU:
https://github.com/neulab/compare-mt/blob/master/scorers.py#L133

We can probably remove all calls to the nltk implementation. It seems like a bad idea to have two different implementations of the same thing from a software engineering point of view.
(CC: @zdou0830)

Description

The report

N sentences where Sys A > Sys B at sentence-level BLEU

will generate wrong output if:

a) Sys A never generates sentences that have a higher BLEU score

b) There are less than N sentences in the set of sentences to be analyzed

Screenshots

Files

To Reproduce

Use the SysA, SysB and Ref outputs located at https://gist.github.com/madaan/2cec36a7b18dfeea3904ddfff1e19312 and run compare-mt with the default options.

Tasks

• Add checks before emitting the report at:

  compare-mt/compare_mt/reporters.py

  Line 507 in 41700d6

  print(f'--- {report_length} sentences where {sright}>{sleft} at {self.scorer.name()}')

I can take a stab at it if you guys think this should be fixed.

Thanks!

Currently compare_mt.py is purely a command line tool. However, it would be nice if it was possible to directly print out HTML reports with graphics that could be used directly in papers.

In order to do this, I think it would be best to change report generation into two steps:

1. A step of collecting all the statistics necessary in some data structure
2. A step of printing out the actual report based on these statistics

If the logic is separated appropriately, then only 2. would have to vary based on the type of report being generated.

It'd be nice if we could also use METEOR. There are examples of how to use METEOR in Python that we might be able to start from:
https://github.com/hharnisc/python-meteor

It looks like maybe we have an encoding issue when printing out non-ASCII characters in output. Because I was mainly looking at English I didn't realize, but it's obvious when printing out the Slovak source sentences included in the example. See attached figure.

See the discussion on the PR here:

#34

It would be best for RIBES to match the official scores (CC: @zdou0830 @pmichel31415).

One thing that MT systems can do is calculate word-by-word likelihoods over the development/test data. It might be nice to be able to do analysis over these likelihoods as well.

Currently the "caption" above tables or figures is based on the parameters input into the model. Unfortunately, not all of the parameters are included in these captions (e.g. freq_corpus_file is not included in the word_accuracies caption), so sometimes it's hard to tell the difference between them. I don't think it's necessarily ideal to put all of the parameters in the caption though, because the parameters can be quite verbose.

One solution to this is to allow "naming" of each type of analysis, where the user can add a name or caption parameter that will then be used as the caption.

It would be convenient for users to be able to pip install compare-mt. I'm willing to take care of this but I'd rather wait until things settle down a little bit (especially if we are going to change the name).

In any case, I'll leave this issue up as a reminder

Would it be good to support analysis over just one system?

Would it be helpful to also print source sentences if the source file is provided?

Pending #6, it might be nice to have the ability to take the aggregate reports, then click on a link that shows individual examples of the outputs of the two systems, maybe with the salient parts highlighted. For example, in the n-gram analysis, it would be nice to be able to click through to places where one system got the n-gram right but the other did not.

Probably due to #40 it's no longer possible to run python compare_mt.py (or python compare_mt/compare_mt.py). I get the following error when I try to do so:

Traceback (most recent call last):
  File "/Users/neubig/work/compare-mt/compare_mt/compare_mt.py", line 6, in <module>
    from . import ngram_utils
ImportError: cannot import name 'ngram_utils'

This is a bit annoying, because it's hard to debug. Is there a way around this? I know in xnmt (https://github.com/neulab/xnmt) we used the python setup.py develop command, which allowed us to run things in place while still supporting installing and setup.py.

Currently the analysis is done purely over word surface forms. It would be nice if we could do analysis in a more abstract context, such as using abstract tags like POS tags. One reference for this is the analysis done by @davidweichiang in this paper: http://www.mt-archive.info/HLT-EMNLP-2005-Chiang.pdf

It might be nice to have some idea of how many reordering errors are occurring, it's not clear exactly how to do this, but we can discuss.

Cf #56

Running

python compare_mt/compare_mt_main.py example/ted.ref.eng example/ted.sys1.eng example/ted.sys2.eng --output_directory output

raises the following exception

Traceback (most recent call last):
  File "/home/travis/virtualenv/python3.7-dev/bin/compare-mt", line 11, in <module>
    load_entry_point('compare-mt==0.1', 'console_scripts', 'compare-mt')()
  File "/home/travis/virtualenv/python3.7-dev/lib/python3.7/site-packages/compare_mt-0.1-py3.7.egg/compare_mt/compare_mt_main.py", line 386, in main
    reporters.generate_html_report(reports, args.output_directory)
  File "/home/travis/virtualenv/python3.7-dev/lib/python3.7/site-packages/compare_mt-0.1-py3.7.egg/compare_mt/reporters.py", line 459, in generate_html_report
    content.append(r.html_content(output_directory))
  File "/home/travis/virtualenv/python3.7-dev/lib/python3.7/site-packages/compare_mt-0.1-py3.7.egg/compare_mt/reporters.py", line 205, in html_content
    self.plot(output_directory, self.output_fig_file, ext)
  File "/home/travis/virtualenv/python3.7-dev/lib/python3.7/site-packages/compare_mt-0.1-py3.7.egg/compare_mt/reporters.py", line 196, in plot
    errs=sys_errs, title='Score Comparison', ylabel=self.scorer.name(),
UnboundLocalError: local variable 'sys_errs' referenced before assignment

Words can be bucketed by external labels (bucket_type=label) or numerical labels (bucket_type=numlabel), but we cannot do this for sentences. It'd be nice if we could.

When doing bucketing of either words or sentences, some of the buckets may have very few examples included, and thus the estimates of statistics may not be very reliable. It would be nice to have some idea of the reliability of these, either through:

1. Outputting the number of counts of words/sentences in each bucket
2. Error bars on the estimates calculated through regular standard deviation, or bootstrapping

Sometimes we may have multiple references. Would it be nice to support analysis over multiple references?

I am not sure if there is an explicit way of setting for custom bucket_cutoffs. Right now the optional arguments are taken as a string, which is further split by a comma, so if I wish to specify a list of cutoffs, it will also be split by comma, and wouldn't work.

Currently significance tests are pretty slow. This is because currently, if we set bootstrap=1000, the program will do the entire process of calculating the score (e.g. BLEU) 1000 times.

There is a faster way to implement bootstrap resampling. It is actually possible to calculate the sufficient statistics to calculate BLEU (e.g. the number of n-gram matches, etc.) just once per sentence and cache them. This would be much faster, but would require that we implement BLEU from scratch. I don't think that this is too hard though.

Do we need to add the case-insensitive options when computing scores and word accuracies?

Currently the code is not very modular, it would be nice to refactor it to be cleaner.

This issue brings up a problem about using so-called "bootstrap resampling test" for evaluating "statistical significance" of machine translation (especially neural MT) methods, and similar generation tasks that are evaluated by MT metrics.

In this criterion, the evaluator will choose several number of generated sentences randomly to simulate the distribution of model outputs, but the evaluator does not consider the variance of the trained model itself.

Consider that we have a baseline model to beat, and our champion model of the proposed methods. The champion model will be produced regardless of recognizing it by authors, e.g., if the model was trained only a few times, any people may not be able to judge if the model is the outlier on the model distribution or not.

In this situation, the "bootstrap resampling test" may judge the model of the proposed method is significantly better, but the evaluation was actually employed for only one model variant which may be a champion, and did not consider any distributional properties of the proposed model.

The "bootstrap resampling test" was introduced on the era of statistical MT, and I guessed the method historically produced reasonable judgements for SMT systems because their study was usually investigating some additions of almost-fixed systems such as Moses (note that I said "almost-fixed" here because they also had random tuning for hyperparameters). In neural MT systems, this assumption had gone because the systems were randomly trained from scratch, and the "bootstrap resampling test" may no longer produce meaningful results but rather give the model a wrong authority.

I was observing continuously that the "bootstrap resampling test" was still utilized in many papers to give "statistical significance" of the model, and strongly worried about misleading this line of research.

Do people think it's helpful to format the CLI outputs using some external packages like tabulate? Please see an example below.

Also, tabulate can take an argument to generate html outputs, which may also be helpful when considering dumping results in html.

Before:

x < -20	0	0
-20 <= x < -10	8	12
-10 <= x < -5	66	67
x = -5	41	44
x = -4	73	58
x = -3	115	97
x = -2	173	187
x = -1	263	256
x = 0	394	423
x = 1	330	344
x = 2	296	275
x = 3	219	194
x = 4	127	153
x = 5	107	93
6 <= x < 11	194	182
11 <= x < 21	39	51
21 < x	0	9

After:

--------------  ---  ---
x < -20           0    0
-20 <= x < -10    8   12
-10 <= x < -5    66   67
x = -5           41   44
x = -4           73   58
x = -3          115   97
x = -2          173  187
x = -1          263  256
x = 0           394  423
x = 1           330  344
x = 2           296  275
x = 3           219  194
x = 4           127  153
x = 5           107   93
6 <= x < 11     194  182
11 <= x < 21     39   51
21 < x            0    9
--------------  ---  ---

ps: If so, adding requirements.txt will be in higher demand. (The package already requires nltk, which has not been stated in README.)

Currently the formatting of numbers is not consistent, and also cannot be changed. It would be nice to adjust the following:

• Most places use four digits, but some use 2, and others use default string formatting of Python. It would be nice to be able to specify the number of digits.
• We can't adjust whether we want to have numbers be multiplied by 100 or not (sometimes this is good for BLEU, as it makes numbers easier to read and tables more concise).
• Integer values should not have decimal places.

This can be worked on once #49 is merged.

Pending the refactoring in #18, it would also be nice to be able to write out LaTeX tables to be automatically copy-pasted into papers.

Errors in formatting of the command line arguments can be hard to understand. For example, if you accidentally put a comma instead of a semicolon between two filenames, you can get something like this:

Traceback (most recent call last):
  File "/Users/neubig/anaconda3/envs/python3/bin/compare-mt", line 11, in <module>
    load_entry_point('compare-mt', 'console_scripts', 'compare-mt')()
  File "/Users/neubig/work/compare-mt/compare_mt/compare_mt_main.py", line 411, in main
    reports.append( (name, [func(ref, outs, **arg_utils.parse_profile(x)) for x in arg]) )
  File "/Users/neubig/work/compare-mt/compare_mt/compare_mt_main.py", line 411, in <listcomp>
    reports.append( (name, [func(ref, outs, **arg_utils.parse_profile(x)) for x in arg]) )
  File "/Users/neubig/work/compare-mt/compare_mt/arg_utils.py", line 4, in parse_profile
    k, v = kv.split('=')
ValueError: not enough values to unpack (expected 2, got 1)

It would be nice to have these be more clear.

Setup has been fixed, but unit tests are failing and need to be checked.

It would be nice to have tests for statistical significance and error bars based on bootstrap re-sampling.

• Paper: http://aclweb.org/anthology/W04-3250
• Example Implementation: https://github.com/neubig/util-scripts/blob/master/paired-bootstrap.py

I think there is a small bug in a way the freq_counts is populated from a freq_corpus_file in the following lines:

The value in word is a list of words not the word itself.

PS: I am working on #10, and I can include this fix in the same PR.

Currently when we are calculating word accuracy, for example, we are calculating an implicit alignment to see which words are matched or not:
https://github.com/neulab/compare-mt/blob/master/bucketers.py#L43

It would be nice to modularize the code that calculates alignments between the reference and the system output, which would allow it to be used for other things like #16 as well.

When using word frequency bucketing, the reporting doesn't output where the frequencies came from, so there is no indication of the difference between different types of word frequency analysis (e.g. train set vs. test set). It would be nice to print out where the frequencies are from.