Bridging Resolution: A Survey of the State of the Art

Proceedings of the 28th International Conference on Computational Linguistics, pages 3708–3721 Barcelona, Spain (Online), December 8-13, 2020 3708 Bridging Resolution: A Survey of the State of the Art Hideo Kobayashi and Vincent Ng Human Language Technology Research Institute University of Texas at Dallas Richardson, TX 75083-0688 {hideo,vince}@hlt.utdallas.edu Abstract Bridging reference resolution is an anaphora resolution task that is arguably more challenging and less studied than entity coreference resolution. Given that significant progress has been made on coreference resolution in recent years, we believe that bridging resolution will receive increasing attention in the NLP community. Nevertheless, progress on bridging resolution is currently hampered in part by the scarcity of large annotated corpora for model training as well as the lack of standardized evaluation protocols. This paper presents a survey of the current state of research on bridging reference resolution and discusses future research directions. 1 Introduction Bridging resolution is an anaphora resolution task that involves identifying and resolving bridg- ing/associative anaphors, which are anaphoric references to non-identical associated antecedents. To better understand the difficulty of the task, consider the following sentences: Even if baseball triggers losses at CBS – and he doesn’t think it will – “I’d rather see the games on our air than on NBC and ABC,” he says. In this example, a bridging link exists between the anaphor the games and its antecedent baseball, as the definite description cannot be interpreted correctly unless it is associated with baseball. Bridging anaphora resolution is arguably more difficult than entity coreference resolution, the task of determining which entity mentions in a text refer to the same real-world entity. For entity coreference resolution, there are well-defined linguistic constraints at the grammatical (e.g., gender and number agreement), syntactic (e.g., binding theory), semantic (e.g., semantic class agreement), and discourse (e.g., centering) levels. Oftentimes, the antecedent of an anaphor can be identified by comparing its lexical similarity with the anaphor. In contrast, there are typically no clear syntactic or other surface clues for identifying the antecedent of a bridging anaphor. It is not uncommon that resolution requires the use of context as well as commonsense inference. Furthermore, while antecedents in entity coreference are noun phrases (NPs), antecedents in bridging can also be non-NPs such as verb phrases (VPs) or clauses, which considerably increase the possible number of candidate antecedents for each anaphor. Bridging resolution is comparatively less studied than entity coreference resolution. Progress on bridg- ing resolution is currently hampered in part by the scarcity of large annotated corpora for model training as well as the lack of standardized evaluation protocols. More specifically, while there are a few bridg- ing corpora that are used more extensively than the others for evaluation purposes, many of which do not have standard train-test partitions. Moreover, these corpora were annotated with somewhat differ- ent definitions of bridging, so good performance on one corpus does not necessarily translate to good performance on another. Worse still, resolvers were evaluated under different settings. For instance, dif- ferent researchers employ different, sometimes undocumented …

Content truncated to 500 words