Scene Restoring for Narrative Machine Reading Comprehension (2020)

Abstract: This paper focuses on machine reading comprehension for narrative passages. Narrative passages usually describe a chain of events. When reading this kind of passage, humans tend to restore a scene according to the text with their prior knowledge, which helps them understand the passage comprehensively. Inspired by this behavior of humans, we propose a method to let the machine imagine a scene during reading narrative for better comprehension. Specifically, we build a scene graph by utilizing Atomic as the external knowledge and propose a novel Graph Dimensional-Iteration Network (GDIN) to encode the graph. We conduct experiments on the ROCStories, a dataset of Story Cloze Test (SCT), and CosmosQA, a dataset of multiple choice. Our method achieves state-of-the-art.

## introduction machine reading comprehension (mrc) is an nlp task designed to evaluate a machine's ability to understand human language. this direction has recently drawn much attention due to the fast development of deep learning techniques and large-scale datasets. as a basic form of mrc, the comprehension of narrative has attracted long-standing interests @xcite @xcite . in this paper, we focus on this kind of mrc task. unlike the other type of text, the narratives usually present a series of events, which are related to a scene in real life. in the field of perception, the scene is a kind of information that flows from a physical environment into a perceptual system @xcite . when reading a narrative instead of being in a physical environment, humans tend to restore the scene in their mind according to the text with their prior knowledge for better perception and comprehension @xcite figure 1 : an example of narrative mrc. specifically, it is an example of story cloze test (sct), where given the first four sentences (s 0 , s 1 , s 2 , s 3 ) of the story, a model is required to select the suitable ending from the candidates (e 0 , e 1 ). the middle part is a presumable description of the scene restored by a human reader. @xcite . the scene restored is an immediate association about the event, and it could be composed of the event itself, the state of the person roles, the possible cause and effect, and so on. to approach human intelligence, an mrc model is supposed to have a similar ability to restore the scene. however, previous work @xcite @xcite pay little attention to this ability of narrative mrc models. figure 1 is an example of narrative mrc. while reading the story sentence by sentence, a human tends to restore a scene in his or her mind as described in the figure. subsequently, the human reader can infer that the suitable ending is e 0 based on information of the scene. unfortunately, a ma-figure 2 : the basic overview of the proposed method chine reader is not endowed with the ability to associate the prior knowledge, and cannot restore the scene according to the original narrative. as a result, it cannot thoroughly understand what happens in the story and possibly make a wrong decision. to address this problem, we proposed a novel method, which can restore the scene and utilize it to understand the narrative passages. firstly, we propose to employ external knowledge as the basic resource for restoring the scene. some previous works @xcite also employ external knowledge in narrative mrc. however, most of them use the concept knowledge from conceptnet @xcite , wordnet @xcite , or other word-centered knowledge bases to obtain the association information for the noun phrases mentioned in the story. this kind of method is able to help the machine to understand what is mentioned in the story, but not what happens in the story. for example, with those methods, given the sentence "the right side of his face was all covered in blood.", the machine understand the noun phrases "right side" "face" and "blood" better, but is still unable to know exactly that a man is hurt, he needs medical assistance, and some others nearby might help him. to this end, we select an event-based knowledge graph, atomic @xcite , as the source of external knowledge. atomic is an atlas of everyday commonsense reasoning. each center node of atomic is an event like "personx's face is covered in blood", and the nodes associated with it are the cause, the effect, and the attribute of the roles of the events. therefore, atomic is beneficial for the machine to know "what happens". secondly, we utilize a structured description to restore the scene. specifically, we build a scene graph based on the original narrative and the knowledge from atomic. compared with the unstructured text, graph data can represent the scene more intuitively. in mrc task, previous works @xcite that utilize structured data generally regard the words or noun phrases as the nodes of the graph. those methods have no specific for narrative mrc, where the events and the roles are the key factors. therefore, we build the scene graph by taking the events, the persons, and the external knowledge of the event as the nodes. meanwhile, we design the connections of the graph from both the perspectives of each event and the whole passage. instead of the typical plane graph, we build a threedimensional graph, which can not only model the relevance among the events in the passage but also retain the unique information of each event. to encode the graph in a targeted manner, we propose graph dimensional iteration network (gdin). gdin can encode the scene graph iteratively and thus obtain the integrated representation of the scene graph. as a result, the machine will understand the narrative more comprehensively and make the decision more precisely. to summarise, inspired by human behaviors, we propose a novel method to restore the scene for narrative mrc. specifically, we introduce event knowledge from atomic @xcite , and build the scene graph to describe the scene. to encode the graph, we propose a novel graph neural network, gdin. we conduct experiments on two datasets, rocstories @xcite and cosmosqa @xcite . the results show that our method achieves state-of-the-art. ## method the overview of our method is shown in figure 2 . our starting point is to let the machine restore the scene like a human while reading narrative passages and then utilize the information from the scene to better comprehension. as shown in figure 2 , given a narrative passage, we firstly obtain the knowledge for the events mentioned in the passage. subsequently, we build a scene graph, a three-dimensional graph, whose nodes contain events, person roles, and event knowledge. the graph is composed of two kinds of plane graphs: one is the inner-event graph, which describes a single event; the other is the cross-event graph, which captures the relevance among the events. meanwhile, we conduct a basic encoding for the narrative passage and the knowledge and then obtain their original representation. by utilizing our proposed graph dimensional-iteration network (gdin), we encode the scene graph from the inner-event graph to cross-event graph iteratively. to this end, we obtain the representation of the scene and then make a prediction based on it. to endow the model with the ability to associate the event-relevant description, we introduce external knowledge from atomic. atomic is an event-based knowledge graph. it contains 24,313 central nodes (i.e., base events) like "personx repels persony's attack". each of them is linked to multiple types of knowledge nodes, such as the effect on personx (e.g., person x's heart races), the cause of perosnx (e.g., x wanted to protect himself), the effect on persony (e.g., y gets hurt) and so on. as those knowledge nodes are also events, there are totally 877,108 event, relation, event triples. ## related work machine reading comprehension: due to the fast development of deep learning techniques and large-scale datasets, machine reading comprehension(mrc) has gained increasingly wide attention over the past few years. @xcite build the multiple-choice dataset mctest, and this dataset encourages the early research of machine reading comprehension, and a strand of mrc models @xcite are inspired by the dataset. hermann et al. (2015) propose a cloze test dataset cnn & daily mail, which is large-scale and more suitable than mctest for deep learning methods. based on this dataset, hermann et al. (2015) proposes an attention-based lstm model named attentive reader, and chen et al. ( 2016 ) simplify this model by directly utilize the query-aware context representations to match the candidate answer. moreover, @xcite release the span extraction dataset, squad, which has become the most popular mrc dataset over recent years. this dataset enlightens a lot of classical mrc model, like bidirectional attention flow (bidaf) @xcite and r-net @xcite . recently, there are some new trends in this field, such as multipassage mrc @xcite , knowledgebased mrc @xcite and multi-hop mrc @xcite . narrative comprehension: understanding narrative is a challenging task in natural language understanding, for the passages contain rich cause and effect relations. a large body of previous works focus on scripts learning @xcite . some previous works addressed script learning by focusing on the narrative cloze test @xcite . story cloze test @xcite is then introduced as a new evaluation framework, and gains wide attention @xcite . besides, recent works present other test frameworks for narrative comprehension, such as multiple choice @xcite and answer generation @xcite . compared with the other complex forms of test, e.g., answer generation, the test frameworks we choose (selecting ending or answer) are more focused on narrative comprehension itself. ## conclusion in this paper, we focus on narrative machine reading comprehension. inspired by human behaviors, we propose a novel method to restore the scene for the narrative passage. specifically, we introduce the event knowledge from atomic and build a three-dimensional graph to describe the scene. to encode the scene graph, we propose graph dimensional-iteration network (gdin). we conduct experiments on two relevant datasets, rocstories and cosmosqa. the result shows our method achieves state-of-the-art. further experimental investigation shows that (1) compared with concept knowledge, the event knowledge we choose is more suitable for narrative mrc; (2) our proposed graph models the scene more effectively than the unstructured text and the unified plane graph do; (3) our proposed gdin encodes the scene graph efficiently by iterating multiple steps.