Event-Location Tracking in Narratives: A Case Study on Holocaust Testimonies (2023)

Abstract: This work focuses on the spatial dimension of narrative understanding and presents the task of event-location tracking in narrative texts. The task intends to extract the sequence of locations where the narrative is set through its progression. We present several architectures for the task that seeks to model the global structure of the sequence, with varying levels of context awareness. We compare these methods to several baselines, including the use of strong methods applied over narrow contexts. We also develop methods for the generation of location embeddings and show that learning to predict a sequence of continuous embeddings, rather than a string of locations, is advantageous in terms of performance. We focus on the test case of Holocaust survivor testimonies. We argue for the moral and historical importance of studying this dataset in computational means and that it provides a unique case of a large set of narratives with a relatively restricted set of location trajectories. Our results show that models that are aware of the larger context of the narrative can generate more accurate location chains. We further corroborate the effectiveness of our methods by showing similar trends from experiments on an additional domain.

## introduction a primary goal of narrative analysis is to represent essential dimensions of stories in a schematic manner. one such essential dimension is the location or sequence of locations, in which the story takes place. in fact, location is such an important element in a story, that the ability to situate a story in a place is often viewed as one of the elements that distinguish narrative text from other types of texts @xcite . characterizing stories by their sequence of locations is also beneficial in that it provides a backbone for alignment between different stories-an important task in its own right (see, e.g., @xcite . nevertheless, and despite the wealth of nlp literature that has studied the expression of locations in texts (see §2), we are not aware of works that attempted to extract the trajectory or sequence of locations where a narrative story takes place. in this work, we focus on transcriptions of holocaust survivor testimonies, given in english. it is difficult to overstate the importance of this dataset for the study and memory of the holocaust. with the inevitable perishing of the last living survivors, there is an imminent need to develop new modes of engagement with the vast amount of holocaust testimonies stored in the archives. the application of nlp technology to the analysis of these testimonies has recently been strongly advocated @xcite . indeed, nlp can aid in allowing researchers to gain insight from the entire collection of testimonies (tens of thousands of them), instead of focusing on smallscale, mostly manual, studies. we further argue that holocaust testimonies have the additional, unique value for nlp, due to its combination of a large number of testimonies and a relatively restricted domain, in terms of themes and locations. this quality stands in contrast to that of typical narrative datasets @xcite . in the narrative framework, event-location tracking is unique in two ways. first, as in @xcite , the focus is on event location, i.e., the location where the event takes place. for example, if a witness recounts events in the ghetto, and mentions someone who came from some town, then although the town's name is a mention of a location, it is not part of the event location. second, the locations in narratives should be viewed as a trajectory. for example, migration from europe to america is not merely a change of location since it follows a common post-war theme of holocaust-related trajectories. the task of event-location tracking is therefore different from entity linking in its desired output -a sequence of segment-wise predictions for an entire document. trajectory extraction can be valuable for visualization and trajectory clustering @xcite . in addition, successful location extraction indicates aspects of long-range narrative understanding, a currently open problem in nlp @xcite . in this work, we present various architectures for location tracking that vary in their degree of context awareness. in one architecture, we use a learnable transition matrix, thus taking into account the adjacent locations. in another, we design a hierarchical transformer that can attend to the whole document. we also design a method to generate embeddings for the locations. we compare our methods to a number of baselines that use strong language models but are limited in the context-length that can be taken into account. we show that our models significantly outperform the baseline models. we find that, in general, models with larger context capabilities have higher performance in location tracking. we also find that the use of location embeddings, trained with additional metadata, can contribute to the performance. for validation, we construct an experiment in an additional domain and show that similar trends arise. in conclusion, we find that the task of eventlocation tracking belongs, both conceptually and empirically, to the level of full narratives. we argue that this is an important step towards the understanding of narratives as full stories. our contributions in this work are as follows: (1) we present the task of event-location tracking for an entire narrative text; (2) we establish an experimental setup for the task, in the domain of holocaust survivor testimonies; (3) we present a number of architectures for the task, capable of document-level processing; (4) we present a method for creating domain-dependent location embeddings, and show its value to the task at hand. ## previous work narrative analysis and segmentation. narrative schema analysis attempts to model the essence of event sequences. it is beneficial to extract a highlevel sequential progression of a long story, giving an overview of the events and allowing alignment between relevant parts. for example, @xcite visualized the frequent topic paths in birth stories using segment-wise topic modeling. to extract an interpretable sequential progression it is necessary to divide the long story into shorter segments. @xcite formulated the task of dividing a long text into segments by scenes. @xcite summarized a shared task for scene segmentation and concluded that the task remains challenging. our work is somewhat similar to scene segmentation since location trajectories induce some type of segmentation and locations are a key component in scenes. nevertheless, the definition of a scene involves other aspects, such as time and plot, making it harder to formalize a prediction task and apply domain knowledge to aid the task. we focus, therefore, on locations only, and seek to extract the trajectory of the whole narrative. for this purpose, we focus on coarse locations (such as cities) and not on detailed scenes (such as specific houses). event locations. some recent works have expressed the significance of event locations for narrative analysis. @xcite formulated a definition of narratives with event locations as a key part. @xcite formulated a task of grounding characters to locations. other works extracted event locations for single events, such as events described in tweets @xcite . however, no previous work, to the best of our knowledge, has studied the trajectory of locations in a full narrative. toponym resolution. toponym resolution describes the task of extracting and identifying location mentions in a text. this task can be seen as a subtask of named entity linking, by considering only location ("gpe") entities. therefore, toponym resolution can be carried out with a generalpurpose entity linker. @xcite released a general-purpose entity linker based on autoregres-sive language generation. as in most entity linkers, the entities are wikidata items. nevertheless, it has been shown that special training for spatial entities has better performance, leveraging specialized location gazetteers such as geonames 2 , which is more detailed than wikidata location items @xcite . @xcite presented a survey about location-specific entity extraction. zhang and bethard (2023) provided a system that first resolves the non-ambiguous location mentions and used them as context for the ambiguous ones. @xcite provided an evaluation platform for toponym resolution. location embeddings. many methods exist for general-purpose text-span embeddings @xcite . specifically for locations, @xcite al. (2022) generated location embeddings from human mobility trajectories using a graph-based approach. kejriwal and szekely (2017) generated location embeddings based on random walks over the geonames location graph. dassereto et al. (2020) proposed evaluation methods for location embeddings. trajectory modeling. in contrast to toponym resolution, which focuses on short mentions, a different line of work seeks to extract document-level trajectories. mathew et al. (2012) modeled human location trajectories by hidden markov models (hmm). sassi et al. (2019) replaced hmms with convolutional neural networks applied on location embeddings. lui et al. (2021) used lstm-based models for pedestrian trajectory prediction. in our work, we propose the extraction of location trajectories for events in texts. there are two key differences between this and the existing work, which focuses on trajectory modeling for lists of location coordinates. first, event locations may not be explicitly mentioned in the text, therefore requiring non-trivial mention extraction. second, locations in texts represent more than simple coordinate values. for example, a concentration camp might be close to a city but they clearly represent different thematic locations. ## task definition our setting is the following: given a text, divided into initial segments, @xmath0, x 2 , ..., x n , we wish to predict a sequence of locations @xmath1, y 2 , . . . y n , such that each location y i corresponds to the text 2 http://geonames.org/ segment x i . the location sequence induces another segmentation that is based on locations. we work with a closed set of locations y, i.e., ∀i.y @xmath2. it is instructive to compare this task to ner for locations. whereas ner is a prediction task at the phrase level, our task's focus is on the document as a long sequence of events. this change of focus implies some unique properties: • in location tracking, the locations are to be assigned to spans of multiple sentences, that describe a locally confined cluster of events, and not to individual phrases as in ner. we do not see much value in marking the exact token-level boundaries of event locations since they are not necessarily clear from the text. in our case, we make a practical choice and use units of multiple sentences, as this is the scope of the available labeled data. ## methods location tracking is a structured prediction task, as there are dependencies between the segment labels y i . for tasks like this, the global context of the document, and not just the individual segments, can be helpful. to this end, we propose three different approaches with different degrees of context awareness. ## results scores for location tracking in holocaust testimonies are in we report one score for the greedy algorithm as we find that adding beam search had very little effect. for comparison, we mention here that fixed prediction for the most common category (which is "cities in germany") gave much worse results: @xmath3.84, @xmath4.07, and @xmath5.11. ## discussion our experiments show that document-level architectures are significantly better at event-location 15 the normalized distance might be larger than 1 if the predicted number of topics is larger than the real number. this normalization is commonly known in the literature as word error rate. ## conclusion we presented the task of event-location tracking in narrative texts and proposed various methods for tackling it. we showed that our methods significantly outperform several baseline methods in terms of the trajectory of locations that the model generates. between architectures, we showed that additional context improves results. we also showed that using location embeddings improves results, in addition to their importance for possible generalization. we corroborate our findings in another domain. in addition to the technical contribution of this work, it also makes important first steps in analyzing spoken testimonies in a systematic, yet ethical, manner. as such, our work contributes to the domain of holocaust and memory studies, designing new methods of browsing and analysis that augment the current efforts of scholars and practitioners alike. more broadly, this work makes a significant contribution to the growing field of computational literary studies by offering new models of narrative analysis, based on event locations.