This paper proposes to study a fine-grained semantic novelty detection task , which can be illustrated with the following example . It is normal that a person walks a dog in the park , but if someone says " A man is walking a chicken in the park , " it is novel . Given a set of natural language descriptions of normal scenes , we want to identify descriptions of novel scenes . We are not aware of any existing work that solves the problem . Although existing novelty or anomaly detection algorithms are applicable , since they are usually topic-based , they perform poorly on our fine-grained semantic novelty detection task . This paper proposes an effective model ( called GAT-MA ) to solve the problem and also contributes a new dataset . Experimental evaluation shows that GAT-MA outperforms 11 baselines by large margins . Novelty or anomaly detection has been an important research topic since 1970s ( Barnett and Lewis , 1994 ) due to numerous applications ( Chalapathy et al . , 2018 ; Pang et al . , 2021 ) . Recently , it has also become important for natural language processing ( NLP ) . Many researchers have studied the problem in the text classification setting ( Fei and Liu , 2016 ; Shu et al . , 2017 ; Xu et al . , 2019 ; Lin and Xu , 2019 ; Zheng et al . , 2020 ) . However , these text novelty classifiers are mainly coarse-grained , working at the document or topic level . Given a text document , their goal is to detect whether the text belongs to a known class or unknown class . This paper introduces a new text novelty detection problem -fine-grained semantic novelty detection . Specifically , given a text description d , we detect whether d represents a semantically novel fact or not . This work considers text data that describe scenes of real-world phenomena in natural language ( NL ) . In our daily lives , we observe different real-world phenomena ( events , activities , situations , etc . ) and often describe these observations ( referred as " scenes " onwards ) in NL to others or write about them . It is quite natural to observe scenes that we have not seen before ( i.e. , novel scenes ) . For example , it is a common scene that " A person walks a dog in the park " , but if someone says " A man is walking a chicken in the park " , it is quite unexpected and novel . Detecting such semantic novelty requires complex conceptual and semantic reasoning over text and thus , is a challenging NLP problem . Note that conceptually , the judgement of the novelty of a scene is subjective and might differ from person to person . However , there are some scenes for which a majority of people have agreement about their novelty . A good example of such majority-view of novelty is the widely-spread meme pictures on social media , which contain novel interactions between objects . In this work , we restrict our research to this majority-based view of novelty and leave the personalized novelty view angle for the future work . In this work , we leverage the captions of images from popular datasets like COCO , Flickr , etc . , to build a semantic novelty detection dataset ( Sec . 3 ) , 1where we consider an image as a scene and the corresponding image captions as different NL descriptions of the scene . Detecting text describing semantically novel observations have many applications , e.g. , recommending novel news , novel images & videos ( based on their text descriptions ) , social media posts and conversations . The problem of semantic novelty detection is defined as follows . Problem Definition : Given a set of natural language descriptions D = { d 1 , d 2 , ..... d n } of common scenes , build a model M using D to score the semantic novelty of a test NL description d with respect to D , i.e. , classifying d into one of the two classes { NORMAL , NOVEL } . " NORMAL " means that d is a description of a common scene and " NOVEL " means d is a description of a se-mantically novel scene . As the detection model M is built only with " NORMAL " class data , the task is an one-class text classification problem . We are unaware of any existing work that can effectively solve this problem . Although existing novelty / anomaly detection and one-class classification algorithms are applicable , since they are coarse-grained or topic-based , they perform poorly on our task ( see Sec . 5 ) . Note that although we focus on solving the problem of semantic novelty detection of NL descriptions of scenes , the proposed task and solution framework are generally applicable to other applications . This paper proposes a new technique , called GAT-MA ( Graph Attention network with Max-Margin loss and knowledge-based contrastive data Augmentation ) to identify NL description sentences of novel scenes . Since our task is at the sentence level and fine-grained , we exploit Graph Attention Network ( GAT ) on the parsed dependency graph of each sentence , which fuses both semantic and syntactic information in the sentence for reasoning with the internal interactions of entities and actions . To enable the model to capture long-range interactions , we stack multiple layers of GATs to build a deep GAT model with multi-hop graph attention . We also create the pseudo novel training data based on the given normal training data through contrastive data augmentation . Thus , GAT-MA is trained with the given original normal scene descriptions and the augmented pseudo novel scene descriptions ( Sec . 4 ) . GAT-MA is evaluated using our newly created Novel Scene Description Detection ( NSD2 ) Dataset . The results show that GAT-MA outperforms a wide range of latest novelty or anomaly detection baselines by very large margins . Our main contributions are as follows : 1 . We propose a new task of semantic novelty detection in text . Whereas the existing work focuses on coarse-grained document-or topiclevel novelty , our task requires fine-grained sentence-level semantic & syntactic analysis . 2 . We propose a highly effective technique called GAT-MA to solve the proposed semantic novelty detection problem , which is based on GAT with dependency parsing and knowledgebased contrastive data augmentation . 3 . We create a new dataset called NSD2 for the proposed task . The dataset can be used as a benchmark dataset by the NLP community . Novelty detection is an important problem because anything novel is of interest . This paper proposed a semantic novelty detection problem and designed a graph attention network based approach ( called GAT-MA ) exploiting parsing and data augmentation to solve the problem . As there is no existing evaluation dataset for the proposed task , an evaluation dataset has been created . Experimental comparisons with a wide range of baselines showed that GAT-MA outperforms them by very large margins .
Existing works on novelty or abnormally detection are coarse-grained only focusing on the document or sentence level as a text classification task.
They propose a fine-grained semantic novelty detection problem where systems detect whether a textual description is a novel fact, coupled with a graph attention-based model.
The proposed model outperforms 11 baseline models on the created dataset from an image caption dataset for the proposed task by large margins.