Multitask Learning-Based Neural Bridging Reference Resolution (2020)

Abstract: We propose a multi task learning-based neural model for resolving bridging references tackling two key challenges. The first challenge is the lack of large corpora annotated with bridging references. To address this, we use multi-task learning to help bridging reference resolution with coreference resolution. We show that substantial improvements of up to 8 p.p. can be achieved on full bridging resolution with this architecture. The second challenge is the different definitions of bridging used in different corpora, meaning that hand-coded systems or systems using special features designed for one corpus do not work well with other corpora. Our neural model only uses a small number of corpus independent features, thus can be applied to different corpora. Evaluations with very different bridging corpora (ARRAU, ISNOTES, BASHI and SCICORP) suggest that our architecture works equally well on all corpora, and achieves the SoTA results on full bridging resolution for all corpora, outperforming the best reported results by up to 36.3 p.p..

## introduction anaphora resolution @xcite @xcite @xcite is the aspect of language interpretation concerned with linking nominal expressions to entities in the context of interpretation (or discourse model). as illustrated by (1) (adapted from @xcite ), nominal expressions can be linked to the context in several ways: corefererence (linking [the bakersfield supermarket], [the business], [its]), bridging or associative reference (linking [the customers] to [the supermarket]) @xcite @xcite , and discourse deixis (linking [the murder] to the event of murdering) @xcite . ## experiments datasets we evaluated our systems on arrau @xcite , is-notes @xcite , bashi @xcite and scicorp @xcite with arrau rst as our primary dataset as it is substantially larger than other datasets (see bridging references are annotated in arrau according to the scheme in @xcite , which covers both what @xcite call 'lexical' and 'referential' bridging. the corpus was used for task 2 of the crac 2018 shared task @xcite , focused on bridging resolution. as done in the crac shared task, we evaluate our system on all three subcorpus rst,trains and pear stories foot_2 . the rst portion of the corpus, consisting of 413 news documents (1/3 of the wsj section of the penn treebank). we used the default train/dev and test subdivisions. the trains and pear portion of the corpus contains 114 dialogues and 20 fictions respectively. since the trains and pear are much smaller we use 10-fold cross validation and report the results on test set to compare with previous work. the isnotes corpus consists of 50 documents from the wsj portion of ontonotes, with 663 bridging pairs annotated as well as fine-grained information status according to the scheme in @xcite . bridging is annotated as one of the information status subclasses. like isnotes, the bashi corpus contains 50 documents from the wsj portion of ontonotes. the dataset has 459 bridging pairs annotated according to a annotation scheme similar to that of the isnotes corpus @xcite . we follow @xcite to mix use the isnotes and bashi corpora foot_3 . the scicorp corpus uses text from a very different domain of scientific texts. the corpus has in total 1366 bridging pairs annotated, again according to its own annotation scheme @xcite . since those corpora are rather small, we used 10-fold cross-validation to evaluate on them. evaluation metrics following, e.g., hou et al., we evaluate on both full bridging resolution and its subtasks (anaphor recognition/antecedent selection). for full bridging resolution and anaphor recognition we report f1 scores. 6 for antecedent selection we report accuracy as it uses gold bridging anaphors. hyperparameters apart from the two parameters introduced by our model (maximum number of antecedents and negative example ratio λ), we use the default settings from @xcite , and replace their elmo settings with the bert settings from @xcite . ## conclusions in this paper we proposed a multi-task neural architecture tackling two major challenges for bridging reference resolution. the first challenge is the lack of very large training datasets, as the largest corpus for bridging reference, arrau @xcite , only contains 5.5k examples, and other corpora are much smaller (the most used corpus for bridging, isnotes @xcite , only contains 663 bridging pairs). the second challenge is that different annotation schemes for bridging are used in different corpora, so designing a system that can be applied to different corpora is complicated. our results on the arrau rst corpus demonstrate that the performance on full bridging resolution and its subtasks can be significantly improved by learning with additional coreference annotations. our multi-task model achieved substantial improvements of 7.3%-13.7% for full bridging resolution and its subtasks when compared with the single task baseline that learns solely on bridging annotations. as a result, our final system achieved sota results in all three tasks. further evaluation on trains, pear, isnotes, bashi and scicorp demonstrates the robustness of our system under changes of annotation scheme and domain. the very same architecture used for arrau rst again achieved sota results on full bridging resolution for all five corpora. overall, our results suggest that coreference is a useful source task for bridging reference resolution, and our neural bridging architecture is applicable to bridging corpora based on different domain or definitions of bridging.