Friend-training: Learning from Models of Different but Related Tasks (2023)

Abstract: Current self-training methods such as standard self-training, co-training, tri-training, and others often focus on improving model performance on a single task, utilizing differences in input features, model architectures, and training processes. However, many tasks in natural language processing are about different but related aspects of language, and models trained for one task can be great teachers for other related tasks. In this work, we propose friend-training, a cross-task self-training framework, where models trained to do different tasks are used in an iterative training, pseudo-labeling, and retraining process to help each other for better selection of pseudo-labels. With two dialogue understanding tasks, conversational semantic role labeling and dialogue rewriting, chosen for a case study, we show that the models trained with the friend-training framework achieve the best performance compared to strong baselines.

## introduction many different machine learning algorithms, such as self-supervised learning @xcite @xcite , semisupervised learning @xcite and weakly supervised learning @xcite , aim at using unlabeled data to boost performance. they have been of even greater interest recently given the amount of unlabeled data available. self-training @xcite is one semi-supervised learning mechanism aiming to improve model performance through pseudo-labeling and has been successfully applied to computer vision @xcite , natural language processing @xcite and other fields @xcite . the main challenge of self-training is how to select high-quality pseudo-labels. current selftraining algorithms mainly focus on a single task * work done when interning at tencent ai lab. when assessing the quality of pseudo-labels and suffer from gradual drifts of noisy instances @xcite . this work is motivated by the observation that learning targets of tasks represent different properties of the inputs, and some properties are shared across the tasks which can be used as supervision from one task to another. such properties include certain span boundaries in dependency and constituency parsing, and some emotion polarities in sentiment analysis and emotion detection. two dialogue understanding tasks, conversational semantic role labeling (csrl) and dialogue rewriting (dr), are also such a pair, with shared properties such as coreference and zero-pronoun resolution. as shown in figure 1 , the rewritten utterance can be used to generate cross-task supervision to the arguments of predicate 喜欢(like). we leverage the cross-task supervision from friend tasks -different but related tasks -as a great criterion for assessing the quality of pseudo-labels. in this work, we propose friend-training, the first cross-task self-training framework. compared to single-task self-training, friend-training exploits supervision from friend tasks for better selection of pseudo-labels. to this end, two novel modules are proposed: (1) a translation matcher, which maps the pseudo-labels of different tasks for one instance into the same space and computes a matching score representing the cross-task matching degree of pseudo-labels from different tasks; (2) an augmented (instance) selector, which leverages both the confidence of pseudo-labels from taskspecific models and the matching score to select instances with pseudo-labels of high quality as new training data. we choose csrl and dr as friend tasks to conduct a case study for friend-training, and specify the translation matcher and augmented selector for friend-training between these tasks. experimental results of domain generalization and few-shot learning show friend-training surpasses both classical and state-of-the-art semi-supervised learning algorithms by a large margin. to summarize, contributions from this work include: ## related work self-training self-training @xcite @xcite is a classical semi-supervised learning framework @xcite which has been widely explored in recent years. the general idea of selftraining is to adopt a trained model to pseudo-label easily acquired unlabeled data and use them to augment the training data to retrain the model iteratively. this paradigm shows promising effectiveness in a variety of tasks: including text classification (mukherjee and awadallah, 2020; @xcite , image classification @xcite , machine translation @xcite and model distillation (mukherjee and hassan awadallah, 2020). co-training @xcite and tri-training @xcite are similar iterative training frameworks to selftraining but with a different number of models or considering different views of the training data, both of which see wide adoption in nlp @xcite @xcite @xcite @xcite . these frameworks aim at improving performance with multiple models trained on one task, without directly leveraging the benefit of supervision from related tasks. multi-task learning multi-task learning @xcite seeks to improve the learning performance of one task with the help of other related tasks, among which two lines of work are related to ours: (1) semi-supervised multi-task learning @xcite combines semi-supervised learning and multi-task learning. @xcite exploited unlabeled data by random walk and used a task clustering method for multi-task learning. @xcite integrated active learning (mackay, 1992) with the model in @xcite to retrieve data that are most informative for labeling. although these works tried to utilize unlabeled data to enhance multi-task learning, our work differs from them in incorporating supervised signals among tasks to select high-quality pseudo-labels for updating models, which is an iterative training process without additional human annotation. conversational semantic role labeling csrl is a task for predicting the semantic roles of predicates in a conversational context. @xcite leveraged relational graph neural networks @xcite to model both the speaker and predicate dependency, achieving some promising results. however, the current dataset @xcite for csrl is limited to mono-domain. high-quality labeled data for new domains are needed to empower more applicable csrl models. dialogue rewriting dr is commonly framed as a sequence-to-sequence problem which suffers large search space issue @xcite . to address it, @xcite cast dr to sequence labeling, transforming rewriting an utterance as deleting tokens from an utterance or inserting spans from the dialogue history into an utterance. @xcite improved the continuous span issue in @xcite by first generating multiple spans for each token and slotted rules and then replacing a fixed number rules with spans. ## friend-training friend-training is an iterative training framework to jointly refine models of several friend tasks. different from self-training, friend-training injects crosstask supervision into the selection of pseudo-labels. we first briefly describe self-training before presenting friend-training. ## friend training between csrl and dr to verify the effectiveness of friend-training, we select two dialogue understanding tasks as friend tasks to conduct friend-training experiments for a case study: conversational semantic role labeling (csrl) and dialogue rewriting (dr). while both require skills such as coreference and zero-pronoun resolution, the two tasks focus on different properties of the dialogue utterance: (1) csrl focuses on extracting arguments of the predicates in the utterance from the whole dialogue history; (2) dr algorithm 1: two-task friend-training input :labeled data sets for two friend tasks, l a , l b ; an unlabeled data set u; task models f a , f b . output :refined f a , f b . pre-train f τ with l τ (τ ∈ a, b); while not until the maximum iteration do ## conclusion we propose friend-training, the first cross-task selftraining framework, which leverages supervision from friend tasks for better selection of pseudolabels. moreover, we provide specific modeling of friend-training between conversational semantic role labeling and dialogue rewriting. experiments on domain generalization and few-shot learning scenarios demonstrate the promise of friendtraining, which outperforms prior classical or stateof-the-art semi-supervised methods by substantial margins.