# Bridging Anaphora Resolution as Question Answering Source: [Bridging Anaphora Resolution as Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:22:05.587Z Authors: Yufang Hou Published: 2020-04-16T19:42:43.000Z ## Abstract Most previous studies on bridging anaphora resolution (Poesio et al., 2004; Hou et al., 2013b; Hou, 2018a) use the pairwise model to tackle the problem and assume that the gold mention information is given. In this paper, we cast bridging anaphora resolution as question answering based on context. This allows us to find the antecedent for a given anaphor without knowing any gold mention information (except the anaphor itself). We present a question answering framework (BARQA) for this task, which leverages the power of transfer learning. Furthermore, we propose a novel method to generate a large amount of "quasi-bridging" training data. We show that our model pre-trained on this dataset and fine-tuned on a small amount of in-domain dataset achieves new state-of-the-art results for bridging anaphora resolution on two bridging corpora (ISNotes (Markert et al., 2012) and BASHI (Roesiger, 2018)). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Bridging Resolution on ISNotes](): Bridging Resolution; ISNotes; Accuracy: 50.1. - [Bridging Resolution on BASHI](): Bridging Resolution; BASHI; Accuracy: 38.7. ## Original sources and implementation [Original paper]() [PDF]()