A Rationale-Centric Framework for Human-in-the-loop Machine Learning (2022)

Abstract: We present a novel rational-centric framework with human-in-the-loop – Rationales-centric Double-robustness Learning (RDL) – to boost model out-of-distribution performance in few-shot learning scenarios. By using static semi-factual generation and dynamic human-intervened correction, RDL, acting like a sensible “inductive bias”, exploits rationales (i.e. phrases that cause the prediction), human interventions and semi-factual augmentations to decouple spurious associations and bias models towards generally applicable underlying distributions, which enables fast and accurate generalisation. Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-distribution tests, especially for few-shot learning scenarios, compared to many state-of-the-art benchmarks. We also perform extensive ablation studies to support in-depth analyses of each component in our framework.

## introduction recent work finds that natural artefacts @xcite or spurious patterns @xcite in datasets can cause sub-optimal model performance for neural networks. as shown in figure 1 , the bold phrases-"100% bad" and "brain cell killing"-are underlying causes for a negative sentiment prediction that most human readers would recognise. these are defined as rationales in this paper. the underlined phrase-"acting and plot"has been incorrectly recognised as a causal term by the model used fort this example, and is referred to as a spurious pattern. spurious patterns (or associations) are caused by natural artefacts or biases in training data @xcite , and are usually useless, or even harmful, at test time. this issue can be severe in few-shot learning (fsl) figure 1 : a negative movie review with human annotated causal terms (bold text) and spurious patterns recognised by the model (underlined text). scenarios. for instance, @xcite suggests that when a model is trained with a small subset of labelled data, it is prone to exploiting spurious patterns leading to poor generalisability that is evident in the performance decay in outof-distribution (ood) datasets. in spite of these issues, training deep neural networks using few labelled examples is a compelling scenario since unlabelled data may be abundant but labelled data is expensive to obtain in real-world applications @xcite . there is a strand of research addressing this scenario that seeks to improve model performance by "introducing methods and resources for training models less sensitive to spurious patterns" @xcite . most of this work relies on generating counterfactual augmented data (cad), either manually @xcite or automatically @xcite @xcite @xcite . for example, @xcite proposed a humanin-the-loop framework where human annotators are required to make minimal changes to original movie reviews to produce sentiment-flipped counterfactual reviews, which enables models to learn useful associations between input texts and output labels @xcite . generating manual counterfactuals, however, is expensive and time-consuming- @xcite report the cost of revising 2.5k instances at over $10,000. on the other hand, fully automatic methods are task-specific and therefore have weak robustness across domains and less reliabil- semi-factual generation (3.3) rationales marking (3.2) augmented training set static semi-factual generation train film is good. i like it. movie is good. i like it false rationales correction (3.4) missed rationales correction (3.4) re-train film was good. i like it. i like it. -> positive film is good. i like it. (positive) example text: false rationales: film is good. ## method the rdl pipeline is shown in figure 2 and consists of two modules: static semi-factual generation and dynamic human-intervened correction. static semi-factual generation is a more efficient alternative to manually generated counterfactuals @xcite . in the first phase, rationale marking (section 3.1), human annotators review each document in the training set to provide rationales (i.e. phrases that support the document classification decisions shown as bold text in figure 2 ). the second phase is a semi-factual generation method based on synonym replacement (section 3.2) that produces augmented examples (blue text in figure 2 indicates replaced words), which are added into the training set. dynamic human-intervened correction (section 3.3) is a rationales-powered human-in-the-loop framework to dynamically correct the model's behaviours. at the outset, sampling and sensitivity of contextual decomposition (scd) @xcite is applied to detect the rationales given by the model that is obtained in the previous step. then, all model-identified rationales (underlined texts in figure 2 ) are examined by human annotators to iden-tify false rationales (i.e. words or phrases that do not support the classifications but are falsely included by the model) and missing rationales (i.e. words or phrases that support the classifications but are not included by the model). both false rationales and missing rationales are corrected to produce augmented examples. finally, newly generated examples are added into the training set to re-train the deep learning model. ## experiments our intention is to improve the generalisability of models, and we use both in-distribution and ood performance for evaluation. our experiments are designed to address the following research questions: ## conclusion we proposed a rationale-centric human-in-the-loop framework, rdl, for better model generalisability in few-shot learning scenarios. experimental results show that our method can boost performance of deep neural networks in both in-distribution and ood datasets and make models less sensitive to spurious patterns, enabling fast generalisation. in the future, we expect to see rationale-centric frameworks defined for different tasks, including ner, question answering, and relation extraction.