Context-aware Information-theoretic Causal De-biasing for Interactive Sequence Labeling (2022)

Abstract: Supervised training of existing deep learning models for sequence labeling relies on large scale labeled datasets. Such datasets are generally created with crowd-source labeling. However, crowd-source labeling for tasks of sequence labeling can be expensive and time-consuming. Further, crowd-source labeling by external annotators may not be appropriate for data that contains user private information. Considering the above limitations of crowd-source labeling, we study interactive sequence labeling that allows training directly with the user feedback, which alleviates the annotation cost and maintains the user privacy. We identify two bias, namely, context bias and feedback bias, by formulating interactive sequence labeling via a Structural Causal Model (SCM). To alleviate the context and feedback bias based on the SCM, we identify the frequent context tokens as confounders in the backdoor adjustment and further propose an entropy-based modulation that is inspired by information theory. entities more sample-efficiently. With extensive experiments, we validate that our approach can effectively alleviate the biases and our models can be efficiently learnt with the user feedback.

## introduction recently, deep learning models have yielded stateof-the-art performance for tasks of sequence labeling, such as pos tagging @xcite and named entity recognition (ner) @xcite @xcite . unfortunately, existing deep learning models are well-known of data-hungry @xcite @xcite , relying on large annotated datasets for training. these datasets are generally created with crowd-source labeling, e.g., using amazon mechanical turk. in a modern ner system, there may be hundreds of entity classes. in example 1, the user gets the top 1 prediction "organization" and a list of other predicted top candidate entities and provides the feedback that "government" is correct. ## interactive sequence labeling in this section, we elaborate the procedures of our interactive sequence labeling on batches of streaming data of the users. specifically, for each batch of the test sequence, the interaction between sequence labeling model and the users can be decomposed into: display, feedback and training. display: we apply the current sequence labeling model on the text sequences @xmath0, . . . , x n ] with length n . for each token @xmath1, we collect the predicted probability distribution over @xmath2, . . . , y m ] with total m token labels, from which we select top k candidate labels based on the predictions and the entropy-based modulation detailed in section 4.1. then, the token e and its selected k candidates {d 1 , . . . , d k } are displayed to the users for feedback. feedback: as discussed in section 1, our interactive sequence labeling involves two types of user feedback: positive feedback and negative feedback. given a token and its k candidate labels, the user will provide a positive feedback via pointing out the ground truth label y if @xmath3, . . . , d k }. otherwise, the user will feedback with "none of the above", i.e., negative feedback. training: the sequence labeling model is periodically trained with the feedback from users over time. for a positive feedback, we train the model with the designed deconfounding layer and the cross-entropy loss with the ground truth label y. if a negative feedback is given, we train the model with the proposed entropy-based modulation and doubly robust estimation, described in section 4.2. ## interactive sequence labeling from a causal view to fundamentally explain the biases and their sources in the interactive learning, we analyse interactive sequence labeling from the causal perspective and propose a structural causal model (scm). ## related work causal inference for sequence labeling previous works @xcite @xcite identified several causal effects in sequence labeling problems including named-entity recognition and part-of-speech tagging. to identify the confounders and analyze spurious correlations in their models, they fundamentally formulate their problems via a structural causal model (scm). further, they employ the backdoor adjustment @xcite @xcite to remove the spurious correlation introduced by the backdoor paths in the scm. active learning given a dataset of unlabeled samples, active learning aims at selecting the most task-informative subset of samples (queries) for labeling, so as to maximize the model performance trained with the acquired labels, while minimizing the annotation cost @xcite @xcite @xcite @xcite . our setting of interactive sequence labeling is different from active learning in the following perspectives: i) unlike active learning that select samples for annotation from an unlabeled dataset, our sequence labeling model is trained with batches of streaming data. ii) we send all the data of each batch to the users, i.e., we do not perform sample selection, but instead focusing on alleviating the context and feedback biases in interactive learning. de-biasing for interactive @xcite studies the task of interactive learning for named entity normalization. however, different from our work, they ignore the bias problems involved with the top k display. previous works @xcite @xcite realized the importance of handling non-displayed events. they regarded non-displayed events as unlabeled instances, and model the ctr prediction as a learning problem with labeled and unlabeled instances, which aims to learn under covariate shift (sample bias corrections). several counterfactual estimators have been developed. importance sampling (is) is a simple way to tackle this issue, but suffers from high variances. classic variance reduction techniques @xcite @xcite for is are useful for counterfactual evaluation and learning. inverse propensity score (ips) @xcite weights each labeled event with the inverse of its propensity score, which is determined by the likelihood of the logged data. doubly robust for counterfactual learning @xcite takes advantage of the ips @xcite and direct method @xcite to increase the chances of accurate ratio estimations. ## conclusion the state-of-the-art sequence labeling models rely on an adequate amount of labeled data. crowdsource labeling for sequence labeling can be expensive, time-consuming, and not be appropriate for data containing user private information. we study how to efficiently and accurately train sequence labeling models directly with the user feedback, with a simple feedback mechanism. moreover, we fundamentally analyze and explain the biases involved in interactive sequence labeling, formulating interactive sequence labeling from a causal view and propose a structural causal model. based on the structural causal model, we learn debiased interactive sequence labeling, via identify the confounders as the most frequent context tokens for the backdoor adjustment and further propose an information-theoretic causal de-biasing method, i.e., entropy-based modulation, by leveraging the relations between the contexts and entity tokens. with extensive evaluations, we validate the effectiveness of our proposed de-biasing approaches.