Teamwork Is Not Always Good: An Empirical Study of Classifier Drift in Class-incremental Information Extraction (2023)

Abstract: Class-incremental learning (CIL) aims to develop a learning system that can continually learn new classes from a data stream without forgetting previously learned classes. When learning classes incrementally, the classifier must be constantly updated to incorporate new classes, and the drift in decision boundary may lead to severe forgetting. This fundamental challenge, however, has not yet been studied extensively, especially in the setting where no samples from old classes are stored for rehearsal. In this paper, we take a closer look at how the drift in the classifier leads to forgetting, and accordingly, design four simple yet (super-) effective solutions to alleviate the classifier drift: an Individual Classifiers with Frozen Feature Extractor (ICE) framework where we individually train a classifier for each learning session, and its three variants ICE-PL, ICE-O, and ICE-PL&O which further take the logits of previously learned classes from old sessions or a constant logit of an Other class as constraint to the learning of new classifiers. Extensive experiments and analysis on 6 class-incremental information extraction tasks demonstrate that our solutions, especially ICE-O, consistently show significant improvement over the previous state-of-the-art approaches with up to 44.7% absolute F-score gain, providing a strong baseline and insights for future research on class-incremental learning.

## introduction conventional supervised learning assumes the data are independent and identically distributed (i.i.d.) and usually requires a pre-defined ontology, which may not be realistic in many applications in natural language processing (nlp). for instance, in event detection, the topics of interest may keep shifting over time (e.g., from attack to pandemic), and new event types and annotations could emerge the figure shows two classifiers that are incrementally trained from session 1 and session 2 and are evaluated on the same sample. after training on session 2, the classifier mistakenly predicts other for an arrest mention due to the classifier drift. the model here uses pre-trained features and only the classifier is trained. in this work, we focus on the class-incremental learning (cil) setting @xcite , where a new session foot_1 is composed of previously unseen classes and the goal is to learn a unified model that performs well in all seen classes. when new learning sessions arrive sequentially, the classification layer must be constantly updated and/or expanded to accommodate new categories to the model. the change of the classifier between different sessions, i.e., classifier drift, can disturb or overwrite the classifier trained on previous classes, which consequently causes catastrophic forgetting @xcite . on the other hand, in many nlp tasks such as information extraction, the model also needs to classify nega-tive instances into the other type (i.e., none-of-theabove). the other type adds extra difficulty to classification, and even worse, the meaning of other varies as the model learns new sessions @xcite . the cil problem thus becomes even more challenging when other is involved. we illustrate the event detection task in cil @xcite and the classifier drift problem in figure 1 . despite the progress achieved in cil @xcite , there are two critical limitations that are still remained: (1) most previous cil approaches heavily rely on the rehearsalbased strategy which stores samples from previously learned sessions and keeps re-training the model on these examples in subsequent sessions to mitigate catastrophic forgetting, which requires high computation and storage costs and raises concerns about privacy and data leakage @xcite ; (2) previous approaches have mainly focused on regularizing or expanding the overall model, especially feature extractor, to tackle the forgetting issue @xcite ), but they rarely investigate whether the drift of the classifier also leads to forgetting, especially in classification tasks that involve the other category. in this work, we aim to tackle these limitations by answering the following two research questions: rq1: how does classifier drift lead to forgetting in the setting where no samples are stored from old sessions for rehearsal?, and rq2: how to devise an effective strategy to alleviate classifier drift, especially when there is an other category involved? in this paper, we aim to answer the two research questions above. first, to study how classifier drift alone affects the model, we build a baseline where we use a pre-trained language model as a fixed feature extractor, such that only the parameters in the classification layer will be updated. second, to alleviate classifier drift, we propose a simple framework named individual classifiers with frozen feature extractor (ice). instead of collectively tuning the whole classification layer, we individually train a classifier for the classes in each new session without updating old classifiers and combine all learned classifiers to classify all seen classes during inference. as individually trained classifiers may lack the context of all learned sessions @xcite , they may not be comparable to each other. we further devise a variant ice-pl which takes the logits of previous classifiers as constraints to encourage contrastivity among all the classes when learning a new classifier for a new session. third, both ice and ice-pl cannot be applied to detection tasks where an other class is involved, thus we further design two variants of them: ice-o and ice-pl&o, which introduce a constant logit for the other class and use it to enforce each individual classifier to be bounded by a constraint shared across different learning sessions during training. we extensively investigate the classifier drift and evaluate our approach on 6 essential information extraction tasks across 4 widely used benchmark datasets under the cil setting. our major findings and contributions are: (1) by comparing the drifted baseline and our ice, we find that the classifier drift alone can be a significant source of forgetting and our approaches effectively mitigate the drift and forgetting. our results reveal that training the classifier individually can be a superior solution to training the classifier collectively in cil. (2) we find that the other type can effectively improve individually trained classifiers, and it is also helpful when we manually introduce negative instances during training on the tasks that do not have other. ## related work existing approaches for cil can be roughly categorized into three types @xcite . rehearsal-based approaches (a.k.a. experience replay) @xcite @xcite @xcite select some previous examples (or generate pseudo examples) for rehearsal in subsequent tasks. while such approaches are effective in mitigating forgetting, they require high computation and storage costs and suffer from data leakage risk @xcite @xcite . regularization-based approaches @xcite aim to regularize the model's update by only updating a subset of parameters. architecturebased approaches @xcite @xcite adap-tively expand the model's capacity via parameterefficient techniques (e.g., adapter, prompt) to accommodate more data. while most existing approaches consider alleviating the forgetting of the whole model or transferring previous knowledge to new sessions, few of them thoroughly investigate how the classification layer of the model is affected as it expands to incorporate more classes into the model. @xcite find that the classification layer has a strong bias towards new classes, but they only study this issue in image recognition that doesn't contain the other class. to fill the blank in current research, we aim to take a closer look at how the drift in the classifier alone affects the model under the cil setting, especially when other is involved. for class-incremental information extraction, several studies tackle the cil problem in relation learning @xcite , and many of them apply prototype-based approaches equipped with memory buffers to store previous samples @xcite @xcite . others investigate how to detect named entities @xcite or event trigger @xcite @xcite in the cil setting. for instance, @xcite propose to distillate causal effects from the other type in continual named entity recognition. one critical disadvantage of existing approaches for continual ie is they heavily rely on storing previous examples to replay, whereas our method does not require any examplar rehearsal. ## problem formulation class-incremental learning requires a learning system to learn from a sequence of learning sessions @xmath0, ..., d t } and each session d @xmath1, y k )|y @xmath2. the label set c k for session d k is not overlapping with that of other sessions, i.e., ∀k, j and k ̸ = j, c k c @xmath3. given a test input x and a model that has been trained on up to t sessions, the model needs to predict a label ŷ from a label space that contains all learned classes, i.e., c 1 ... c t and optionally the other class. generally, the training instances in old classes are not available in future learning sessions. we consider a learning system consisting of a feature extractor and a classifier. specifically, we use a linear layer g 1:@xmath4, where c is the number of classes that the model has learned up to session t and h is the hidden dimension size of features. we denote the number of classes in a learning session k as n k , i.e., n @xmath5. the classification layer g 1:t can be viewed as a concatenation of the classifiers in all learned sessions, i.e., g 1:@xmath6; ...; w t ], where each of the classifier w @xmath7. the linear layer outputs the logits o 1:@xmath8, where o k refers to the logits for the classes in c k . the term logit we use in this paper refers to the raw scores before applying the softmax normalization. in this work, we focus on studying the classincremental problem in information (entity, relation, and event) extraction tasks.we consider two settings for each task: the detection task that requires the model to identify and classify the candidate mentions or mention pairs into one of the target classes or other, and the classification task that directly takes the identified mentions or mention pairs as input and classifies them into the target classes without considering other. ## conclusion in this paper, we investigate the answers and the solutions to the research questions that how the classifier drift alone affects a model in the classincremental learning setting, and how to alleviate the drift without retraining the model on previous examples. we, therefore, propose to train a classifier individually for each task and combine them together during inference, such that we can maximally avoid the drift in the classifier. extensive experiments show that our proposed approaches significantly outperform all the considered baselines on both class-incremental classification and detection benchmarks and provide super-strong baselines. we hope this work can shed light on future research on continual learning in broader research communities.