Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction (2021)

Abstract: Distantly supervised relation extraction (RE) automatically aligns unstructured text with relation instances in a knowledge base (KB). Due to the incompleteness of current KBs, sentences implying certain relations may be annotated as N/A instances, which causes the so-called false negative (FN) problem. Current RE methods usually overlook this problem, inducing improper biases in both training and testing procedures. To address this issue, we propose a two-stage approach. First, it finds out possible FN samples by heuristically leveraging the memory mechanism of deep neural networks. Then, it aligns those unlabeled data with the training data into a unified feature space by adversarial training to assign pseudo labels and further utilize the information contained in them. Experiments on two wildly-used benchmark datasets demonstrate the effectiveness of our approach.

## introduction relation extraction (re), defined as the task of identifying relations from unstructured text given two entity mentions, is a key component in many nlp applications such as knowledge base (kb) population @xcite and question answering @xcite . supervised re requires a large amount of human labeled data, which is often labor-intensive and time-consuming. distant supervision (ds), proposed by @xcite , deals with this problem by aligning a large corpus with a kb such as freebase to provide weak supervision. it relies on the assumption that if two entities participate in a relation in a kb, then any sentence containing this entity pair expresses this relation. however, this assumption is too strong and brings in noises, particularly when the training kb is an external information source and not primarily derived from the training corpus. to alleviate neighborhood generative adversarial networks (gan) @xcite is first introduced into re by @xcite to learn a sentence-level fp filter. however, they only focus on the fp problem and their model cannot be generalized to other scenarios. @xcite attempt to solve the fn problem using entity descriptions from wikipedia to filter fn samples from the n/a set and further utilize gan in a semi-supervised manner. however, their method heavily depends on external resources and is not applicable to all situations. moreover, their filtering heuristic "two entities mention each other on their wikipedia pages may imply the predefined relations" is even stronger than that of @xcite . it may filter some noisy data but at the same time introducing even more noises. for example, in the sentence "... to the poetry from @xcite that stretches the book ..." excerpted from nyt10, india and korea mention each other on wikipedia, but this sentence implies no relation. in this paper, we propose a novel two-stage approach for distantly supervised re. in the first stage, called mining, we find out possible fn samples from the n/a set by heuristically leveraging the memory mechanism of deep neural networks. according to @xcite , while deep neural networks are capable of memorizing noisy data, they tend to preferentially learn simple patterns first, that is to say, deep neural networks tend to learn and memorize patterns from clean instances within a noisy dataset. we design a transformerbased @xcite deep filter to mine fn samples from the n/a set. in the second stage, called aligning, we formulate the problem into a domain adaptation (da) paradigm. we exploit a gradient reversal layer (grl) to align the mined unlabeled data from stage one with the training data into a unified feature space. after aligning, each sentence is assigned with a pseudo label and a confidence score, which provide extra information and attenuate incorrect biases in both training and testing procedures. in summary, our main contributions are fourfold: distant supervision. supervised re requires a large amount of human-labeled training data, which is labor-intensive and time-consuming. to address this limitation, mintz et al. (2009) propose ds by heuristically aligning a text corpus to a kb. riedel et al. (2010) relax ds for multi-instance singlelabel learning. subsequently, to handle the overlapping relations between entity pairs, hoffmann et al. (2011); surdeanu et al. (2012) propose the multiinstance multi-label learning paradigm. currently, ds is already a common practice in re. neural relation extraction. the above works strongly rely on the quality of hand-engineered features. zeng et al. (2014) first propose an end-to-end cnn-based neural network to automatically capture relevant lexical-level and sentence-level features. zeng et al. (2015); lin et al. (2016) further improve this through piecewise max pooling and selective attention @xcite . @xcite propose an attention-based lstm to capture the most important semantic information in sentences. external knowledge like entity descriptions and type information has been used for re @xcite . pre-trained language models contain a notable amount of semantic information and commonsense knowledge, and several works have applied them to re @xcite . adversarial training. adversarial training is a machine learning technique that improves the networks using an adversarial objective function or deceptive samples. @xcite bring it in re by adding adversarial noises to the training data. @xcite propose dsgan to learn a generator that explicitly filters fp instances from the training dataset. @xcite propose a semi-distant supervision method by first splitting a dataset through entity descriptions and then using gan to make full use of unsupervised data. @xcite learn the distribution of true positive instances through adversarial training and select valid instances via a rank-based model. learning with noisy labels. as noisy labels degrade the generalization performance of deep neural networks, learning from noisy labels (a.k.a. robust training) has become an important task in modern deep learning @xcite . @xcite find out that, although deep networks are capable of memorizing noisy data, they tend to learn simple patterns first. co-teaching @xcite trains two deep neural networks simultaneously and lets them teach each other given every mini-batch. yan et al. (2016); nguyen et al. (2020); li et al. (2020) transform the problem into a semisupervised learning task by treating possibly falselabeled samples as unlabeled. ## methodology in this section, we introduce our two-stage framework called fan (false negative adversarial networks) in detail. first, we describe how we discover possibly wrong-labeled sentences from the n/a set. then, we introduce our adversarial da method, which assigns pseudo labels to unlabeled data with confidence scores. ## experiment setup we conduct the experiments on two widely used benchmark datasets: nyt10 and gids (google-iisc distant supervision dataset). the source code is publicly available foot_0 . ## conclusion and future work in this paper, we propose fan, a two-stage method using adversarial da to handle the fn problem in distantly supervised re. we mine fn samples using the memory mechanism of deep neural networks. we use grl to align unlabeled data with training data and generate pseudo labels to correct improper biases in both training and testing procedures. our experiments show the superiority of fan against many comparative models. in future work, we plan to use the teacher-student model to deal with fp and fn simultaneously.