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Yvette Graham

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2 papers

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ECAI Conference 2024 Conference Paper

REFINE-LM: Mitigating Language Model Stereotypes via Reinforcement Learning

Rameez Qureshi
Naïm Es-Sebbani
Luis Galárraga
Yvette Graham
Miguel Couceiro
Zied Bouraoui

With the introduction of (large) language models, there has been significant concern about the unintended bias such models may inherit from their training data. A number of studies have shown that such models propagate gender stereotypes, as well as geographical and racial bias, among other biases. While existing works tackle this issue by preprocessing data and debiasing embeddings, the proposed methods require a lot of computational resources and annotation effort while being limited to certain types of biases. To address these issues, we introduce REFINE-LM, a debiasing method that uses reinforcement learning to handle different types of biases without any fine-tuning. By training a simple model on top of the word probability distribution of a LM, our bias agnostic reinforcement learning method enables model debiasing without human annotations or significant computational resources. Experiments conducted on a wide range of models, including several LMs, show that our method (i) significantly reduces stereotypical biases while preserving LMs performance; (ii) is applicable to different types of biases, generalizing across contexts such as gender, ethnicity, religion, and nationality-based biases; and (iii) it is not expensive to train.

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AAAI Conference 2018 Conference Paper

Translating Pro-Drop Languages With Reconstruction Models

Longyue Wang
Zhaopeng Tu
Shuming Shi
Tong Zhang
Yvette Graham
Qun Liu

Pronouns are frequently omitted in pro-drop languages, such as Chinese, generally leading to signiﬁcant challenges with respect to the production of complete translations. To date, very little attention has been paid to the dropped pronoun (DP) problem within neural machine translation (NMT). In this work, we propose a novel reconstruction-based approach to alleviating DP translation problems for NMT models. Firstly, DPs within all source sentences are automatically annotated with parallel information extracted from the bilingual training corpus. Next, the annotated source sentence is reconstructed from hidden representations in the NMT model. With auxiliary training objectives, in terms of reconstruction scores, the parameters associated with the NMT model are guided to produce enhanced hidden representations that are encouraged as much as possible to embed annotated DP information. Experimental results on both Chinese–English and Japanese–English dialogue translation tasks show that the proposed approach signiﬁcantly and consistently improves translation performance over a strong NMT baseline, which is directly built on the training data annotated with DPs.

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