CFL: Causally Fair Language Models Through Token-level Attribute Controlled Generation

We propose a method to control the attributes of Language Models (LMs) for the text generation task using Causal Average Treatment Effect (ATE) scores and counterfactual augmentation. We explore this method, in the context of LM detoxification, and propose the Causally Fair Language (CFL) architecture for detoxifying pre-trained LMs in a plug-and-play manner. Our architecture is based on a Structural Causal Model (SCM) that is mathematically transparent and computationally efficient as compared with many existing detoxification techniques. We also propose several new metrics that aim to better understand the behaviour of LMs in the context of toxic text generation. Further, we achieve state of the art performance for toxic degeneration, which are computed using \RTP (RTP) benchmark. Our experiments show that CFL achieves such a detoxification without much impact on the model perplexity. We also show that CFL mitigates the unintended bias problem through experiments on the BOLD dataset.Comment: 19 pages, 10 figures. Findings of ACL 202

Co-regularized Alignment for Unsupervised Domain Adaptation

Deep neural networks, trained with large amount of labeled data, can fail to generalize well when tested with examples from a \emph{target domain} whose distribution differs from the training data distribution, referred as the \emph{source domain}. It can be expensive or even infeasible to obtain required amount of labeled data in all possible domains. Unsupervised domain adaptation sets out to address this problem, aiming to learn a good predictive model for the target domain using labeled examples from the source domain but only unlabeled examples from the target domain. Domain alignment approaches this problem by matching the source and target feature distributions, and has been used as a key component in many state-of-the-art domain adaptation methods. However, matching the marginal feature distributions does not guarantee that the corresponding class conditional distributions will be aligned across the two domains. We propose co-regularized domain alignment for unsupervised domain adaptation, which constructs multiple diverse feature spaces and aligns source and target distributions in each of them individually, while encouraging that alignments agree with each other with regard to the class predictions on the unlabeled target examples. The proposed method is generic and can be used to improve any domain adaptation method which uses domain alignment. We instantiate it in the context of a recent state-of-the-art method and observe that it provides significant performance improvements on several domain adaptation benchmarks.Comment: NIPS 2018 accepted versio

Causal Graphs Underlying Generative Models: Path to Learning with Limited Data

Training generative models that capture rich semantics of the data and interpreting the latent representations encoded by such models are very important problems in unsupervised learning. In this work, we provide a simple algorithm that relies on perturbation experiments on latent codes of a pre-trained generative autoencoder to uncover a causal graph that is implied by the generative model. We leverage pre-trained attribute classifiers and perform perturbation experiments to check for influence of a given latent variable on a subset of attributes. Given this, we show that one can fit an effective causal graph that models a structural equation model between latent codes taken as exogenous variables and attributes taken as observed variables. One interesting aspect is that a single latent variable controls multiple overlapping subsets of attributes unlike conventional approach that tries to impose full independence. Using a pre-trained RNN-based generative autoencoder trained on a dataset of peptide sequences, we demonstrate that the learnt causal graph from our algorithm between various attributes and latent codes can be used to predict a specific property for sequences which are unseen. We compare prediction models trained on either all available attributes or only the ones in the Markov blanket and empirically show that in both the unsupervised and supervised regimes, typically, using the predictor that relies on Markov blanket attributes generalizes better for out-of-distribution sequences