Distantly Supervised Relation Extraction using Multi-Layer Revision Network and Confidence-based Multi-Instance Learning (2021)

Abstract: Distantly supervised relation extraction is widely used in the construction of knowledge bases due to its high efficiency. However, the automatically obtained instances are of low quality with numerous irrelevant words. In addition, the strong assumption of distant supervision leads to the existence of noisy sentences in the sentence bags. In this paper, we propose a novel Multi-Layer Revision Network (MLRN) which alleviates the effects of word-level noise by emphasizing inner-sentence correlations before extracting relevant information within sentences. Then, we devise a balanced and noise-resistant Confidence-based Multi-Instance Learning (CMIL) method to filter out noisy sentences as well as assign proper weights to relevant ones. Extensive experiments on two New York Times (NYT) datasets demonstrate that our approach achieves significant improvements over the baselines.

## introduction relation extraction (re), which aims to classify the relations between a pair of entities in a sentence, is crucial to various applications like questionanswering and construction of knowledge bases. however, supervised relation extraction requires large amounts of manually labeled training data, which is hard to obtain. therefore, @xcite proposed distantly supervised relation extraction (dsre) to automatically generate training data by aligning the knowledge base with text corpus. however, dsre is based on the strong assumption that for an entity pair participating in a relation in the knowledge base, all sentences mentioning this entity pair in the corpus express the same relation. this brings a large number of noisy sentences into the generated data. the worst case is the noisy bag problem where all the sentences in the bag are mislabeled. on the other hand, low-quality sentences in the corpus contain a large proportion of irrelevant words, meaning even correctly labeled sentences may be filled with inner-sentence noise. to better present the impact of both sentence-level noise and word-level noise (inner-sentence noise), we select a sentence bag from new york times (nyt) corpus as shown in figure 1 . among the three sentences, only s2 expresses the label relation, meaning s1 and s3 are both noisy sentences. what's worse, in s2, the relation is indicated by a single word co-founders and the rest of the words can be regarded as noise. to tackle sentence-level noise, various multiinstance learning @xcite methods are proposed to reduce the effects of noisy sentences. some methods filter out noisy sentences and keep the relevant ones @xcite @xcite , but they may filter out relevant sentences as well. some other methods apply soft labels or weights to limit the impact of noisy sentences @xcite @xcite , yet still at risk of being influenced by sentence-level noise because of the soft method. therefore, a more balanced multi-instance learning strategy should be designed to avoid noisy sentences as well as fully exploit information in relevant ones. to address word-level noise, a robust encoder is needed for capturing relevant information in a noisy context. most of the previous work uses encoders based on recurrent neural network (rnn) or convolutional neural network (cnn) @xcite . however, both rnn and cnn models have shortcomings. in rnn encoders, irrelevant words can easily spread noise to its context since they are not isolated from relevant ones. as for cnn encoders, they may lose the salient information because of their pooling layers. to sum up, previous methods are not able to handle noisy words without information loss. based on the idea that noisy words have weaker correlations with others, we try to fill this gap by modeling the correlations within sentences using attention mechanism. we propose a novel multi-layer revision network (mlrn) with confidence-based multi-instance learning (cmil) to tackle both wordlevel and sentence-level noise. in mlrn, we employ the novel revision layers, which alleviate noise by emphasizing inner-sentence correlations, to extract relevant information from the sentence. in each revision layer, we first model the correlations between words using self-attention, then emphasize the correlations by revising the attention weights, and finally apply a translation query (trq) for information extraction. by stacking multiple revision layers, implicit correlations between words are addressed. to alleviate sentence-level noise and tackle the noisy bag problem, we devise a confidence vector to measure the relevance of sentences to the relation classes and further utilize it to guide sentence filtering and weighting. our contributions can be summarized as follows: ## related work distant supervision (ds) for relation extraction @xcite is proposed for efficient knowledge base construction. however, ds brings about the wrong labeling problem as well. riedel et al. (2010) proposes multi-instance learning for dsre to address this issue. most of the current work uses two types of mil strategies: to remove noisy sentences or to apply soft weights. following the at-least-one assumption, zeng et al. (2015) selects the instance with the highest probability within the bag. qin et al. (2018) and feng et al. (2018) employ reinforcement learning for instance selection. for better information utilization, lin et al. (2016) applies selective attention on the sentence level to dynamically adjust the attention weights. yuan et al. (2019a) calculates the similarity between instances and the best sentence. ds may create noisy bags where all the sentences are mislabeled. yuan et al. (2019b) and ye and ling (2019) use bag-level attention to address this issue. cnn-based @xcite and rnnbased @xcite networks are widely used for capturing information within the sentences. in addition, @xcite and @xcite integrate extra linguistic information into the model to address word-level noise. since attention mechanism has been proved effective for modeling longrange dependencies in the sequence @xcite , attention-based models @xcite @xcite are also introduced into dsre. in our work, we further make use of attention mechanism to emphasize correlations between words and devise a balanced confidence-based strategy to address noisy sentences. ## methodology the overall structure of our model is shown in figure 2 . our model can be divided into three parts: embedding layer, revision network and multiinstance learning layer. in this section, we introduce them respectively. ## experiments in this section, we first introduce the datasets and evaluation metrics used in the experiments. then, we provide our experimental settings. afterwards, we compare our model with baselines using the evaluation metrics. finally, we discuss the effects of the revision layer and the cmil strategy. ## case study in figure 6 , we select a bag of instances from test set and present their assigned weights in different models . among the four sentences, s1, s2 and s4 are all relevant to the relation. but in s4, the relation is indicated in an implicit way by the word "israeli". s3 is an irrelevant sentence that does not mention the nationality of the entity david_bengurion. as the example shows, all the three methods are able to correctly handle the irrelevant sentence s3. although selfatt+cmil works fine when s1 uses the phrase "israel 's first leader", it wrongly filters out s4 when encountered with the phrase "israeli leaders". it is because that selfatt+cmil is unable to detect the relevance between "israel" and "israeli" in s4. the selective attention method is designed to exploit relevant sentences, but in an attempt to avoid sentence-level noise, it may also down-weight the relevant sentences it has less confidence in, such as s1 and s4. our complete model (mlrn+cmil) successfully detects the relevance between "israel" and "israeli", therefore regards s4 as a relevant sentence. moreover, mlrn+cmil assigns more balanced weights to relevant sentences comparing with other methods. this example verifies mlrn's ability to capture implicit correlations between sentences. it also proves that cmil not only alleviates sentence-level noise, but also makes further progress in information utilization. ## conclusion and future work in this paper, we propose a novel mlrn+cmil model for distantly supervised relation extraction. the mlrn structure is able to alleviate noise by modeling inner-sentence correlations and extract relevant information. the cmil strategy is a balanced and robust way to avoid noisy sentences as well as assign proper weights to relevant ones. the experimental results show that our approach achieves significant improvements over the baselines and is effective in handling both word-level and sentence-level noise. in the future, we will try to extend our confidence-based method to bag-level, and experiment with the novel revision network on other tasks to further prove its effectiveness.