#StackBounty: #natural-language #model-evaluation #validation #rouge #bleu Shouldn't ROUGE-1 precision be equal to BLEU with w=(1, …

Bounty: 50

I am trying to evaluate a NLP model using BLEU and ROUGE. However, I am a bit confused about the difference between those scores. While I am aware that ROUGE is aimed at recall whilst BLEU measures precision, all ROUGE implementations I have come across also output precision and the F-score. The original ROUGE paper only briefly mentions precision and the F-score, therefore I am a bit unsure about what meaning they have to ROUGE. Is ROUGE mainly about recall and the precision and F-score are just added as a compliment, or is the ROUGE considered to be the combination of those three scores?

What confuses me even more is that to my understanding ROUGE-1 precision should be equal to BLEU when using the weights (1, 0, 0, 0), but that does not seem to be the case.
The only explanation I could have for this is the brevity penalty. However, I checked that the accumulated lengths of the references are shorter than the length of the hypothesis, which means that the brevity penalty is 1.
Nonetheless, BLEU with w = (1, 0, 0, 0) scores 0.55673 while ROUGE-1 precision scores 0.7249.

What am I getting wrong?

I am using nltk to evaluate BLEU and rouge-metric for ROUGE.

Disclaimer: I already posted this question on Data Science, however after not receiving any replies and doing some additional research on the differences between Data Science and Cross Validated, I figured that this question might be better suited for Cross Validated (correct me if I am wrong).


Get this bounty!!!

#StackBounty: #natural-language #model-evaluation #validation #rouge #bleu Shouldn't ROUGE-1 precision be equal to BLEU with w=(1, …

Bounty: 50

I am trying to evaluate a NLP model using BLEU and ROUGE. However, I am a bit confused about the difference between those scores. While I am aware that ROUGE is aimed at recall whilst BLEU measures precision, all ROUGE implementations I have come across also output precision and the F-score. The original ROUGE paper only briefly mentions precision and the F-score, therefore I am a bit unsure about what meaning they have to ROUGE. Is ROUGE mainly about recall and the precision and F-score are just added as a compliment, or is the ROUGE considered to be the combination of those three scores?

What confuses me even more is that to my understanding ROUGE-1 precision should be equal to BLEU when using the weights (1, 0, 0, 0), but that does not seem to be the case.
The only explanation I could have for this is the brevity penalty. However, I checked that the accumulated lengths of the references are shorter than the length of the hypothesis, which means that the brevity penalty is 1.
Nonetheless, BLEU with w = (1, 0, 0, 0) scores 0.55673 while ROUGE-1 precision scores 0.7249.

What am I getting wrong?

I am using nltk to evaluate BLEU and rouge-metric for ROUGE.

Disclaimer: I already posted this question on Data Science, however after not receiving any replies and doing some additional research on the differences between Data Science and Cross Validated, I figured that this question might be better suited for Cross Validated (correct me if I am wrong).


Get this bounty!!!

#StackBounty: #natural-language #model-evaluation #validation #rouge #bleu Shouldn't ROUGE-1 precision be equal to BLEU with w=(1, …

Bounty: 50

I am trying to evaluate a NLP model using BLEU and ROUGE. However, I am a bit confused about the difference between those scores. While I am aware that ROUGE is aimed at recall whilst BLEU measures precision, all ROUGE implementations I have come across also output precision and the F-score. The original ROUGE paper only briefly mentions precision and the F-score, therefore I am a bit unsure about what meaning they have to ROUGE. Is ROUGE mainly about recall and the precision and F-score are just added as a compliment, or is the ROUGE considered to be the combination of those three scores?

What confuses me even more is that to my understanding ROUGE-1 precision should be equal to BLEU when using the weights (1, 0, 0, 0), but that does not seem to be the case.
The only explanation I could have for this is the brevity penalty. However, I checked that the accumulated lengths of the references are shorter than the length of the hypothesis, which means that the brevity penalty is 1.
Nonetheless, BLEU with w = (1, 0, 0, 0) scores 0.55673 while ROUGE-1 precision scores 0.7249.

What am I getting wrong?

I am using nltk to evaluate BLEU and rouge-metric for ROUGE.

Disclaimer: I already posted this question on Data Science, however after not receiving any replies and doing some additional research on the differences between Data Science and Cross Validated, I figured that this question might be better suited for Cross Validated (correct me if I am wrong).


Get this bounty!!!

#StackBounty: #natural-language #model-evaluation #validation #rouge #bleu Shouldn't ROUGE-1 precision be equal to BLEU with w=(1, …

Bounty: 50

I am trying to evaluate a NLP model using BLEU and ROUGE. However, I am a bit confused about the difference between those scores. While I am aware that ROUGE is aimed at recall whilst BLEU measures precision, all ROUGE implementations I have come across also output precision and the F-score. The original ROUGE paper only briefly mentions precision and the F-score, therefore I am a bit unsure about what meaning they have to ROUGE. Is ROUGE mainly about recall and the precision and F-score are just added as a compliment, or is the ROUGE considered to be the combination of those three scores?

What confuses me even more is that to my understanding ROUGE-1 precision should be equal to BLEU when using the weights (1, 0, 0, 0), but that does not seem to be the case.
The only explanation I could have for this is the brevity penalty. However, I checked that the accumulated lengths of the references are shorter than the length of the hypothesis, which means that the brevity penalty is 1.
Nonetheless, BLEU with w = (1, 0, 0, 0) scores 0.55673 while ROUGE-1 precision scores 0.7249.

What am I getting wrong?

I am using nltk to evaluate BLEU and rouge-metric for ROUGE.

Disclaimer: I already posted this question on Data Science, however after not receiving any replies and doing some additional research on the differences between Data Science and Cross Validated, I figured that this question might be better suited for Cross Validated (correct me if I am wrong).


Get this bounty!!!

#StackBounty: #natural-language #model-evaluation #validation #rouge #bleu Shouldn't ROUGE-1 precision be equal to BLEU with w=(1, …

Bounty: 50

I am trying to evaluate a NLP model using BLEU and ROUGE. However, I am a bit confused about the difference between those scores. While I am aware that ROUGE is aimed at recall whilst BLEU measures precision, all ROUGE implementations I have come across also output precision and the F-score. The original ROUGE paper only briefly mentions precision and the F-score, therefore I am a bit unsure about what meaning they have to ROUGE. Is ROUGE mainly about recall and the precision and F-score are just added as a compliment, or is the ROUGE considered to be the combination of those three scores?

What confuses me even more is that to my understanding ROUGE-1 precision should be equal to BLEU when using the weights (1, 0, 0, 0), but that does not seem to be the case.
The only explanation I could have for this is the brevity penalty. However, I checked that the accumulated lengths of the references are shorter than the length of the hypothesis, which means that the brevity penalty is 1.
Nonetheless, BLEU with w = (1, 0, 0, 0) scores 0.55673 while ROUGE-1 precision scores 0.7249.

What am I getting wrong?

I am using nltk to evaluate BLEU and rouge-metric for ROUGE.

Disclaimer: I already posted this question on Data Science, however after not receiving any replies and doing some additional research on the differences between Data Science and Cross Validated, I figured that this question might be better suited for Cross Validated (correct me if I am wrong).


Get this bounty!!!

#StackBounty: #natural-language #model-evaluation #validation #rouge #bleu Shouldn't ROUGE-1 precision be equal to BLEU with w=(1, …

Bounty: 50

I am trying to evaluate a NLP model using BLEU and ROUGE. However, I am a bit confused about the difference between those scores. While I am aware that ROUGE is aimed at recall whilst BLEU measures precision, all ROUGE implementations I have come across also output precision and the F-score. The original ROUGE paper only briefly mentions precision and the F-score, therefore I am a bit unsure about what meaning they have to ROUGE. Is ROUGE mainly about recall and the precision and F-score are just added as a compliment, or is the ROUGE considered to be the combination of those three scores?

What confuses me even more is that to my understanding ROUGE-1 precision should be equal to BLEU when using the weights (1, 0, 0, 0), but that does not seem to be the case.
The only explanation I could have for this is the brevity penalty. However, I checked that the accumulated lengths of the references are shorter than the length of the hypothesis, which means that the brevity penalty is 1.
Nonetheless, BLEU with w = (1, 0, 0, 0) scores 0.55673 while ROUGE-1 precision scores 0.7249.

What am I getting wrong?

I am using nltk to evaluate BLEU and rouge-metric for ROUGE.

Disclaimer: I already posted this question on Data Science, however after not receiving any replies and doing some additional research on the differences between Data Science and Cross Validated, I figured that this question might be better suited for Cross Validated (correct me if I am wrong).


Get this bounty!!!

#StackBounty: #natural-language #model-evaluation #validation #rouge #bleu Shouldn't ROUGE-1 precision be equal to BLEU with w=(1, …

Bounty: 50

I am trying to evaluate a NLP model using BLEU and ROUGE. However, I am a bit confused about the difference between those scores. While I am aware that ROUGE is aimed at recall whilst BLEU measures precision, all ROUGE implementations I have come across also output precision and the F-score. The original ROUGE paper only briefly mentions precision and the F-score, therefore I am a bit unsure about what meaning they have to ROUGE. Is ROUGE mainly about recall and the precision and F-score are just added as a compliment, or is the ROUGE considered to be the combination of those three scores?

What confuses me even more is that to my understanding ROUGE-1 precision should be equal to BLEU when using the weights (1, 0, 0, 0), but that does not seem to be the case.
The only explanation I could have for this is the brevity penalty. However, I checked that the accumulated lengths of the references are shorter than the length of the hypothesis, which means that the brevity penalty is 1.
Nonetheless, BLEU with w = (1, 0, 0, 0) scores 0.55673 while ROUGE-1 precision scores 0.7249.

What am I getting wrong?

I am using nltk to evaluate BLEU and rouge-metric for ROUGE.

Disclaimer: I already posted this question on Data Science, however after not receiving any replies and doing some additional research on the differences between Data Science and Cross Validated, I figured that this question might be better suited for Cross Validated (correct me if I am wrong).


Get this bounty!!!

#StackBounty: #natural-language #model-evaluation #validation #rouge #bleu Shouldn't ROUGE-1 precision be equal to BLEU with w=(1, …

Bounty: 50

I am trying to evaluate a NLP model using BLEU and ROUGE. However, I am a bit confused about the difference between those scores. While I am aware that ROUGE is aimed at recall whilst BLEU measures precision, all ROUGE implementations I have come across also output precision and the F-score. The original ROUGE paper only briefly mentions precision and the F-score, therefore I am a bit unsure about what meaning they have to ROUGE. Is ROUGE mainly about recall and the precision and F-score are just added as a compliment, or is the ROUGE considered to be the combination of those three scores?

What confuses me even more is that to my understanding ROUGE-1 precision should be equal to BLEU when using the weights (1, 0, 0, 0), but that does not seem to be the case.
The only explanation I could have for this is the brevity penalty. However, I checked that the accumulated lengths of the references are shorter than the length of the hypothesis, which means that the brevity penalty is 1.
Nonetheless, BLEU with w = (1, 0, 0, 0) scores 0.55673 while ROUGE-1 precision scores 0.7249.

What am I getting wrong?

I am using nltk to evaluate BLEU and rouge-metric for ROUGE.

Disclaimer: I already posted this question on Data Science, however after not receiving any replies and doing some additional research on the differences between Data Science and Cross Validated, I figured that this question might be better suited for Cross Validated (correct me if I am wrong).


Get this bounty!!!

#StackBounty: #cross-validation #model-selection #model-evaluation #overfitting #generalization-error What is accepted practice for avo…

Bounty: 50

This is an extension of a previous question:
How to avoid overfitting bias when both hyperparameter tuning and model selecting?
…which provided some options for the question at hand, but now I would like to pivot to knowing what is accepted practice or rule of thumb.

In short, say we do hyperparameter tuning on multiple ML model families. The following selection step of choosing the model family itself provides another opportunity for optimistic bias. This could be resolved by some of the strategies noted in the link above.

Noting the previous discussion, are there accepted rules of thumb (or research) on when said strategies are important? For instance, if just optimizing two model families, is it generally safe to ignore the concern and pick the model family in the train split score (or perhaps even the test split)? Or is there a certain n number of model families at which this becomes a danger and tripple-nesting or gridsearch modifications of some kind is needed?


Get this bounty!!!