FAIRFLOW: Mitigating Dataset Biases through Undecided Learning for Natural Language Understanding
Authors: Jiali Cheng, Hadi Amiri


Reviewer paper summaries: This paper presents a novel method to mitigate dataset bias called FAIRFLOW based on undecided learning. The authors categorize explicit and implicit bias modeling into 4 distinct types: Ungrammatical perturbation, Sub-input perturbation, Model-based perturbation, and Representations-based perturbation. Through a series of experiments, the proposed method outperforms seven baselines in four different settings (in-domain, stress, out-of-distribution, and transfer test sets) across three datasets. Also, the method demonstrates its potential expandability, showing the improved performance when applying it to previous debiasing objectives such as product of experts (PoE) and debiased focal loss.
Summary of strengths: 1. The paper introduces a **novel method** that draws biased predictions closer to uniform distribution to prevent definite judgments from fragmentary patterns called shortcuts. It formulates the problem as undecided learning which is not implemented in previous research. 
2. The sufficient analysis and discussion, particularly in **[Section 4 Results and Discussions]**, are valuable for further research. 
3. The **future expandability** of the proposed method especially in transferability, OOD, and different previous debiasing objectives demonstrates that this paper highly achieves its research goal which is generalized and robust bias mitigation.
Summary of weaknesses: There are a few issues with this paper, which require a detailed explanation or revision to address. 

### Experimental Scores 
The experimental result scores on page 6 have several errors and some parts need detailed explanation for clarity. The paper would be more clear and helpful to understand if there were detailed information on the result scores. 

- Lines 420-422: It might be more accurate to clarify that the scores are averaged scores across three datasets. 
- Lines 442-443: Similarly, the scores need clarification that they are averaged scores across three datasets. 
- Line 428: READ shows an ID performance drop of 1.6 compared to FINETUNE on QQP, not 0.4. 
- Line 464: FAIRFLOW-POE seems to achieve on average 1.1, 3.9, and 2.3 absolute points improvement on ID, stress, and OOD test set across three datasets compared to E2E-POE, not 1.8, 9.5, and 3.8. 
- Lines 469-470: Similarly, FAIRFLOW-POE seems to achieve 0.9, 5.1, and 4.1 points improvement on ID, stress, and OOD test sets across three datasets compared to DEBIASMASK, not 1.5, 12.3, and 11.0. 

### Performance Measures 
In Table 2, MNLI uses Accuracy but QQP and PGR use F1 scores. Several analyses in this paper use average scores across three datasets, so it requires a reference paper that takes an average between accuracy and f1 scores. 

### Lack of Examples 
When discussing the bias modeling in Section 2.3.1, it is hard to understand the proposed perturbation methods in detail, without examples of each perturbation method. It would be helpful if there were several examples of each perturbation method in table format in the Appendix.
Comments, suggestions and typos: ### Incorrect Citations 
It is general to remove parentheses when the citation is used as a functional part of the sentence. To be specific, lines 355, 381, 385, 388, 396, and 401 need to remove parentheses. For example, in line 355, "from (Naik et al., 2018)" is required to be modified into "from Naik et al. (2018)". 

### Subsection Titles 
Are both explicit perturbation and implicit perturbations categorized as explicit biases? I recommend modifying the name of **Section 2.3.1 Explicit Biases** into **Section 2.3.1 Explicit Perturbations** and adding a subsection named **Section 2.3.2 Implicit Perturbations** in line 222 to clearly distinguish explicit perturbation and implicit perturbation methods. 

### Table
On pages 15-16, there is only one small table in each page. It requires editing for readability. Also, in Table 8, the row of FINETUNE needs an upper borderline. 

### Typos 
- Line 203: "hen" -> "then".
- Line 303: "Equations (2)-(4)" -> "Equations (1)-(4)"
Overall assessment scores: 3.5