Source-linked AI summary
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation
Emily Dinan, Angela Fan, Adina Williams, Jack Urbanek, Douwe Kiela, Jason Weston
TL;DR
The paper asks how gender bias in dialogue data affects generative dialogue models and develops mitigation methods for the highly male-biased LIGHT dataset. It combines counterfactual augmentation, targeted data collection, and bias-controlled training, finding that the combined approach yields less gendered and safer responses while maintaining engaging dialogue.
Problem
Gender bias in dialogue data can be reflected and amplified by generative models, while dialogue bias has received less attention than bias in other NLP settings.
Method
The authors measure gender bias across dialogue datasets, focus on LIGHT, and apply counterfactual augmentation, Positive-Bias Data Collection, and Bias Controlled text generation.
Results
The combined methods produce engaging dialogue with measurably less gender bias and offensive content, while LIGHT models otherwise generate male-gendered words nearly 78% of the time on female-only gold responses.
Takeaways & Limitations
Bias mitigation is most effective when data augmentation, positively targeted collection, and bias-controlled training are used together.
Takeaways & Limitations
The gendered-word lists may be incomplete and omit common LIGHT terms such as “hag” and “wench.”
Abstract
from arXiv · showhide
Models often easily learn biases present in the training data, and their predictions directly reflect this bias. We analyze gender bias in dialogue data, and examine how this bias is actually amplified in subsequent generative chit-chat dialogue models. We measure gender bias in six existing dialogue datasets, and focus on the most biased one, the multi-player text-based fantasy adventure dataset LIGHT, as a testbed for our bias mitigation techniques. The LIGHT dataset is highly imbalanced with respect to gender, containing predominantly male characters, likely because it is entirely collected by crowdworkers and reflects common biases that exist in fantasy or medieval settings. We consider three techniques to mitigate gender bias: counterfactual data augmentation, targeted data collection, and bias controlled training. We show that our proposed techniques mitigate gender bias in LIGHT by balancing the genderedness of generated dialogue utterances and are particularly effective in combination. We quantify performance using various evaluation methods---such as quantity of gendered words, a dialogue safety classifier, and human studies---all of which show that our models generate less gendered, but equally engaging chit-chat responses.
1 Introduction
The paper examines how training-data gender bias appears in dialogue and can be amplified by generative models, focusing on the especially male-biased LIGHT dataset. It proposes three mitigation approaches and finds their combination most effective for reducing bias while preserving engaging dialogue.
- Dialogue models can reflect and exacerbate harmful biases present in their training data, making dialogue debiasing increasingly important.
- LIGHT is selected as a testbed because it is more male-biased than comparable dialogue datasets and contains potential bias sources in characters, personas, and dialogues.
- The study analyzes biased personas and dialogues in LIGHT, including gender-stereotyped domestic roles and derogatory references to women.
- The authors investigate counterfactual data augmentation, Positive-Bias Data Collection, and Bias Controlled text generation as mitigation techniques.
- Combining the techniques produces engaging responses with measurably less gender bias and offensive content.
2 Related Work
Prior NLP research has extensively studied gender bias in word-level representations, while sentence and dialogue debiasing received less attention. Existing sentence-level work largely emphasized measurement, motivating a different approach to mitigating bias in dialogue.
- Gender-bias research has documented disparities across many NLP systems and text-based tasks.
- Compared with word-level debiasing, relatively little prior work focused on sentence representations.
- Most sentence-level debiasing work at the time foregrounded measuring bias rather than directly mitigating it.
- The authors distinguish their approach from extending word-embedding postprocessing methods to sentences.
3 Measuring Bias
The paper measures gender imbalance in dialogue datasets, characters, personas, and human-generated utterances, finding pronounced male bias in LIGHT. It then uses these measurements to motivate LIGHT as the primary mitigation testbed.
- Character gender was annotated from personas, assigning neutral when the persona did not make gender explicit.
- LIGHT contains over 1.6 times as many male characters as female characters, unlike the nearly balanced gender distribution in ConvAI2.
- LIGHT personas include gender-biased descriptions, such as assigning cleaning and cooking duties to a girl character.
- The study examines how biases in characters and personas may propagate into dialogues created by crowdworkers playing those personas.
- LIGHT has a male bias of 73%, making it the most gender-imbalanced dataset among the compared dialogue datasets.
4 Mitigating Bias in Generative Dialogue
The paper uses LIGHT as a testbed for mitigating gender bias in generative dialogue through data augmentation, targeted collection, and controllable generation. These methods address bias at the persona, dialogue-data, and model-output levels.
- Counterfactual Data Augmentation: The authors extend counterfactual data augmentation to dialogue by automatically swapping gendered words in copied training dialogues.The swaps use paired words from an existing gendered-word list.
- Positive-Bias Data Collection: Positive-Bias Data Collection balances personas through human-created gender-swapped and diversified characters, then collects additional dialogues from them.Annotators are instructed to preserve grammaticality and create independent female characters rather than only changing gender references.
- Positive-Bias Data Collection: 507 new dialogues containing 6,658 utterances are added through targeted collection, representing approximately 6% of the original dataset size.The added dialogues use gender-balanced personas and encourage annotators to assume equality between genders in the fantasy setting.
- Bias Controlled Text Generation: Bias Controlled generation bins each response by whether it contains female- and male-gendered words, then conditions training and inference on a corresponding control token.At inference time, changing the selected bin can target gender-neutral or otherwise specified genderedness patterns.
- Bias Controlled Text Generation: Across conditioning bins, the ALL debiasing model radically changes generated genderedness while maintaining performance measured by F1.The generation experiments fine-tune a pre-trained Transformer on LIGHT dialogues and use beam search for final generations.
5 Results
Transformer models amplify gender bias in LIGHT, while combining debiasing methods produces less gendered and safer dialogue without reducing engagingness.
- Methods: The ALL model combines a baseline with counterfactual augmentation, Positive-Bias Data Collection, and Bias Controlled training.Five Transformer models were trained, including one model for each method and the combined ALL model.
- Gender Bias: Nearly 78% of gendered words generated for female-only gold utterances are male-gendered, showing amplification beyond dataset bias.The analysis measures gendered-word percentage, male-gendered-word percentage, and F1 overlap across four genderedness bins.
- Debiasing Results: Each mitigation method improves percent gendered words, percent male bias, and F1 over the baseline, with the combined ALL model most advantageous.Positive-Bias Data Collection does not perform as well despite using more data, while Bias Ctrl and ALL yield gender ratios closest to ground truth.
- Bias Control: Bias Controlled training radically changes generated-text genderedness across control bins while causing only small differences in overall F1.Manipulating the bins controls the male bias of generated dialogue.
- Generated Vocabulary: The ALL model includes queen among its top 20 generated words, whereas the baseline's only two gendered nouns are knight and king.Gendered nouns are marked in the top-word analysis after stop-word removal.
- Safety: The ALL model is rated less offensive than both the baseline and gold labels by a Transformer-based dialogue safety classifier.The classifier was fine-tuned for offensive-language classification and applied to generated utterances and human test-set labels.
- Human Evaluation: Human evaluators find ALL generations harder to classify by speaker gender while judging them equally engaging to baseline responses.The result is significant at p < 0.01 for gender prediction difficulty, with no reported engagingness degradation.
6 Discussion
The discussion identifies limitations in gender measurement and generated-dialogue quality, while noting that bias control can support finer control over generated properties.
- Generality of Gendered Words: The gendered-word lists may omit common LIGHT terms such as hag and wench, and future work should represent gender more continuously.The paper therefore limits the comprehensiveness of its gender measurements.
- More Fine-Grained Control: Bias Controlled training controls the quantity of gendered words through control bins and could be adapted to other properties using a sexism or bias classifier.The proposed mechanism is presented as general beyond gendered-word lists.
- Quality of Generated Dialogue: Generative dialogue models overuse frequent words and produce generic utterances, an effect the authors also observe and link to bias.This is described as the “I don’t know” problem.
7 Conclusion
The paper concludes that combining data augmentation, targeted positive-bias collection, and bias-controlled training yields less gendered, safer, balanced, and engaging dialogue.
- Conclusion: The combined methods produce less gendered, more gender-balanced, safer utterances while maintaining engaging dialogue with humans.The conclusion also emphasizes addressing bias from data collection through training-time control.