End-to-end Neural Information Status Classification (2021)

Abstract: Most previous studies on information status (IS) classification and bridging anaphora recognition assume that the gold mention or syntactic tree information is given (Hou et al., 2013; Roesiger et al., 2018; Hou, 2020; Yu and Poesio, 2020). In this paper, we propose an end-to-end neural approach for information status classification. Our approach consists of a mention extraction component and an information status assignment component. During the inference time, our system takes a raw text as the input and generates mentions together with their information status. On the ISNotes corpus (Markert et al., 2012), we show that our information status assignment component achieves new state-of-the-art results on fine-grained IS classification based on gold mentions. Furthermore, our system performs significantly better than other baselines for both mention extraction and fine-grained IS classification in the end-to-end setting. Finally, we apply our system on BASHI (Roesiger, 2018) and SciCorp (Roesiger, 2016) to recognize referential bridging anaphora. We find that our end-to-end system trained on ISNotes achieves competitive results on bridging anaphora recognition compared to the previous state-of-the-art system that relies on syntactic information and is trained on the in-domain datasets (Yu and Poesio, 2020).

## introduction information status (is henceforth) studies how discourse entities are referred to in a text @xcite @xcite and is related to a wide range of discourse phenomena, such as coreference @xcite , bridging @xcite , and comparative anaphora @xcite . in general, is reflects the accessibility of a discourse entity based on the evolving discourse context and the speaker's assumption about the hearer's knowledge and beliefs. based on @xcite and @xcite , @xcite proposed an is scheme for written english which consists of eight is categories. according to @xcite , a mention is a discourse entity that carries information status. an entity is old if it refers to the same entity that has been mentioned previously. mediated entities are discourse-new and hearer-old. they have not been introduced in the discourse directly, but are inferrable from previously mentioned entities, or generally known to the hearer. finally, an entity is new if it is introduced into the discourse for the first time and the hearer cannot infer it from previously mentioned entities. figure 1 explains each is category with examples from a short text. unlike coreference resolution, most previous work on is classification and bridging resolution assumes that the gold mention information is given @xcite @xcite @xcite . on a few corpora where gold mentions are not annotated (e.g., bashi), researchers rely on syntactic parsers to extract mentions @xcite . in this paper, we demonstrate that mentions can be reliably extracted without relying on syntactic information. note that we extract all singleton as well as non-singleton mentions and assign information status to them. this is different from most previous work on coreference resolution @xcite @xcite where the mention extraction component is focused on identifying non-singleton mentions only. our system consists of two models. the first model (menext) uses context-dependent boundary representations to extract mentions of discourse entities including singleton mentions. unlike @xcite , our model does not have the constraint of the maximum mention length, and it reasons over the space of all spans during the inference time. the second model (isassign) assigns infor-s1: [the bakersfield supermarket] new went bankrupt [last may] m/worldknow . s2: [the business located in [northern manhattan] m/worldknow ] old closed when @xcite old owner] m/syntactic was murdered. s3: @xcite m/bridging expressed [outrage] new at [the murder] old . s4: unfortunately, [such crimes] m/comparative are not unusual. s5: in [[brooklyn] m/worldknow and [the bronx] m/worldknow ] m/aggregate , [one out of four commercial firms] new is burglarized [each year] new . s6: since [1984] m/worldknow , [[new york city] m/worldknow 's crime rate] m/syntactic has increased by [62%] m/function . description example old coreferent with an already introduced entity the business located in northern manhattan, its, the murder m/syntactic syntactically linked to other old or mediated mentions its owner, new york city's crime rate m/worldknow. generally known to the hearer last may, northern manhattan, brooklyn, the bronx, new york city, 1984 m/bridging associative anaphors which link to previously introduced related entities/events friends m/comparative usually contain a premodifier to indicate that this entity is compared to another entity such crimes m/aggregate coordinated nps where at least one element is old or mediated brooklyn and the bronx m/function refer to a value of a previously explicitly mentioned rise/fall function 62% new introduced into the discourse for the first time and not known to the hearer before the bakersfield supermarket, outrage, one out of four commercial firms, each year mation status to the extracted mentions. we use a mention's boundary representations in its corresponding context to predict its information status. the context information is adapted from @xcite which is syntactic-agnostic. for information status classification based on gold mentions, our second model isassign significantly outperforms the existing state-of-the-art model @xcite on the isnotes benchmark based on the same context information. in the endto-end setting, our system achieves strong results for both mention extraction and is classification compared to other baselines on isnotes. we further demonstrate the usefulness of our system by applying it to identify referential bridging anaphora on bashi @xcite and scicorp @xcite . we find that our end-to-end system trained on isnotes achieves competitive results for bridging anaphora recognition compared to a recent approach @xcite which uses syntactic tree information for mention extraction and is trained on the in-domain datasets. we will release the code and the trained models at https: //github.com/ibm/bridging-resolution/. ## method in this section, we describe our system for endto-end information status classification in detail. figure 2 illustrates how the system extracts mentions from the input sentences (mention extraction model) and assigns information status for the predicted mentions (is assignment model). ## analysis to better understand the strengths and weaknesses of our system, we provide both quantitative and qualitative analyses. in the following discussion, we use predictions from isnotes. ## conclusions in this paper, we propose a system that addresses information status classification in the end-to-end setting for the first time. our mention extraction model does not require any pruning process and can still achieve strong performance for longer spans. we show that our system outperforms other baselines for mention extraction and is assignment in different settings. we further demonstrate that our system trained on isnotes can be applied to identify bridging anaphors in different domains. in the future, we plan to further improve our algorithm for bridging anaphora recognition. applying our system to detect mentions and anaphoricity for coreference resolution is another interesting avenue for future work.