Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data (2023)

Abstract: Jointly extracting entity pairs and their relations is challenging when working on distantly-supervised data with ambiguous or noisy labels. To mitigate such impact, we propose uncertainty-aware bootstrap learning, which is motivated by the intuition that the higher uncertainty of an instance, the more likely the model confidence is inconsistent with the ground truths. Specifically, we first explore instance-level data uncertainty to create an initial high-confident examples. Such subset serves as filtering noisy instances and facilitating the model to converge fast at the early stage. During bootstrap learning, we propose self-ensembling as a regularizer to alleviate inter-model uncertainty produced by noisy labels. We further define probability variance of joint tagging probabilities to estimate inner-model parametric uncertainty, which is used to select and build up new reliable training instances for the next iteration. Experimental results on two large datasets reveal that our approach outperforms existing strong baselines and related methods.

## introduction joint extraction involves extracting multiple types of entities and relations between them using a single model, which is necessary in automatic knowledge base construction @xcite . one way to cheaply acquire a large amount of labeled data for training joint extraction models is through distant supervision (ds) @xcite . ds involves aligning a knowledge base (kb) with an unlabeled corpus using hand-crafted rules or logic constraints. due to the lack of human annotators, ds brings a large proportion of noisy labels, e.g., over 30% noisy instances in some cases @xcite , making it impossible to learn useful features. the noise can be either false relations due to the aforementioned rule-based matching assumption or wrong entity tags due to limited coverage over entities in open-domain kbs. existing distantly-supervised approaches model noise relying either on heuristics such as reinforcement learning (rl) @xcite and adversarial learning @xcite , or pattern-based methods @xcite to select trustable instances. nevertheless, these methods require designing heuristics or hand-crafted patterns which may encourage a model to leverage spurious features without considering the confidence or uncertainty of its predictions. in response to these problems, we propose unbed-uncertainty-aware bootstrap learning for joint extraction on distantly-supervised data. unbed assumes that 1) low data uncertainty indicates reliable instances using a pre-trained language model (plm) in the initial stage, 2) model should be aware of trustable entity and relation labels regarding its uncertainty after training. our bootstrap serves uncertainty as a principle to mitigate the impact of noise labels on model learning and validate input sequences to control the number of training examples in each step. particularly, we quantify data uncertainty of an instance according to its winning score @xcite and entropy @xcite . we define averaged maximum probability that is estimated by a joint plm over each token in a sequence to adapt previous techniques in joint extraction scheme. instances with low data uncertainty are collected to form an initial subset, which is used to tune the joint plm tagger and facilitate fast convergence. then, we define parametric uncertainty in two perspectives-inter-model and innermodel uncertainty. the former is quantified by selfensembling @xcite and serves as a regularizer to improve model robustness against noisy labels during training. the latter is captured by probability variance in mc dropout @xcite for selecting new confident instances for the next training iteration. such two-fold model uncertainties reinforce with each other to guide the model to iteratively improve its robustness and learn from reliable knowledge. to address the challenge of learning from ds, pre-trained transformers (e.g., bert, gpt-2) have gain much attention. they model strong expressive context-aware representations for text sequence through multiple attention layers, and achieve stateof-the-art performance on various nlp tasks @xcite @xcite . they can be cheaply fine-tuned to solve different downstream tasks including ner and rc. specifically, bert is trained on large english corpus using masked language modeling. the multi-head attention weights indicate interactions between each pair of words and its hidden states integrate semantic information of the whole sentence, which are used to decode different tagging results. uncertainty methods uncertainty generally comes from two sources-aleatoric uncertainty and epistemic uncertainty. the former is also referred to as data uncertainty, describing noise inherent in the data generation. methods mitigating such uncertainty include data interpolation @xcite , winning score, and temperature scale @xcite . the latter is also called model uncertainty, describing whether the structure choice and model parameters best describe the data distribution. one main solution to mitigate model uncertainty is bayesian neural network (bnn) @xcite that puts a prior distribution on its weights. to save computational cost, monte carlo • backbone: bert + self-matching + crf decoder • bert encode a sentence x into token-level representations h • for each query p, self-matching is applied to calculate the position-attention between token at p and each token at target position t • crf considers the correlations between labels in neighborhoods and jointly decodes the best chain of labels dropout is proposed as an approximation of variational bayesian inference @xcite , realized by training models with dropout layers and testing with stochastic inference to quantify probability variance. besides bnn, selfensembling @xcite which measures the outputs variance between models with the same architecture has been shown effective to reduce parametric uncertainty across models. ## joint extraction model tagging scheme for an input sequence @xmath0, ..., x n }, we tag n sequences according to different query position p following @xcite . if p is the start of an entity (query entity e 1 ), the sequence is an instance. the entity type is labeled at p and other entities e 2 which have relationship with the query entity are labeled with relation types re. the rest of tokens are labeled "o" (outside), meaning they do not correspond to the query entity. accordingly, we convert joint extraction into a token classification task and extract relation triplets {e 1 , re, e 2 } in each instance, as shown in figure 1 . position-attentive encoder we use bert @xcite to encode a sentence x into tokenlevel representations @xmath1, .., h n }, where h @xmath2. for each query p, self-matching is applied to calculate the position-attention a @xmath3, which compares the sentence representations against itself to collect context information @xcite . the produced position-aware representation c @xmath4. finally, we concatenate h t and c t to generate position-aware and context-aware representations u @xmath5. crf decoder @xcite for each position-aware representation u t , we first learn a linear transformation z @xmath6. here c is the number of distinct tags. for an instance with labels @xmath7, ..., y n }, the decoding score s(z, y) is the sum of transition score from tag y t to tag y t+1 plus the input score z yt t . the conditional probability p(y|z) is the softmax over s(z, y) for all possible label sequences y ′ . we maximize the loglikelihood of correct tag sequences during training l @xmath8. ## uncertainty-aware bootstrap learning motivation one of the main challenges in bootstrap learning is to evaluate the "correctness" of a labeled instance. we consider this problem from an uncertainty perspective and assume instances with lower uncertainty are more likely to be correctly labeled. in this section, we first propose instancelevel data uncertainty which is used to filter noisy examples and build an initial subset. then, we introduce our two-fold model uncertainties which helps iteratively mitigate ds effect and build up trustable examples during bootstrap learning. ## conclusions in this paper, we propose a novel uncertaintyaware bootstrap learning framework for distantlysupervised joint extraction. specifically, we define data uncertainty in generally token classification to filter out highly-error-prone instances and build an initial high-confident subset, which is used to tune the joint extraction model for fast convergence. we then propose a two-fold bootstrap learning procedure which iteratively mitigates the ds impact on model robustness and selects new trustable training instances. experimental results on two benchmark datasets show that unbed significantly out-performs other denoising techniques.