Korea, Republic of

Humans process natural language online, whether reading a document or participating in multiparty dialogue. Recent advances in neural coreference resolution have focused on offline approaches that assume the full communication history as input. This is neither realistic nor sufficient if we wish to support dialogue understanding in real-time. We benchmark two existing, offline, models and highlight their shortcomings in the online setting. We then modify these models to perform online inference and introduce rollback: a short-term mechanism to correct mistakes. We demonstrate across five English datasets the effectiveness of this approach against an offline and a naive online model in terms of latency, final document-level coreference F1, and average running F1.

COLING 2022

Online Coreference Resolution with Rollback

online

coreference resolution

dialogue

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!<br><br><br>

To gain access to this event page you are required to register.<br> Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

workshop paper

The ever growing amount of research publications demands computational assistance for everyone trying to keep track with scientific processes. Topic modeling has become a popular approach for finding scientific topics in static collections of research papers. However, the reality of continuously growing corpora of scholarly documents poses a major challenge for traditional approaches. We introduce RollingLDA for an ongoing monitoring of research topics, which offers the possibility of sequential modeling of dynamically growing corpora with time consistency of time series resulting from the modeled texts. We evaluate its capability to detect research topics and present a Shiny App as an easy-to-use interface. In addition, we illustrate usage scenarios for different user groups such as researchers, students, journalists, or policy-makers.

Finding Scientific Topics in Continuously Growing Text Corpora

Recently introduced transformer-based article encoders (TAEs) designed to produce similar vector representations for mutually related scientific articles have demonstrated strong performance on benchmark datasets for scientific article recommendation. However, the existing benchmark datasets are predominantly focused on single domains and, in some cases, contain easy negatives in small candidate pools. Evaluating representations on such benchmarks might obscure the realistic performance of TAEs in setups with thousands of articles in candidate pools. In this work, we evaluate TAEs on large benchmarks with more challenging candidate pools. We compare the performance of TAEs with a lexical retrieval baseline model BM25 on the task of citation recommendation, where the model produces a list of recommendations for citing in a given input article. We find out that BM25 is still very competitive with the state-of-the-art neural retrievers, a finding which is surprising given the strong performance of TAEs on small benchmarks. As a remedy for the limitations of the existing benchmarks, we propose a new benchmark dataset for evaluating scientific article representations: Multi-Domain Citation Recommendation dataset (MDCR), which covers different scientific fields and contains challenging candidate pools.

Large-scale Evaluation of Transformer-based Article Encoders on the Task of Citation Recommendation

Product reviews may have complex discourse including coreference and bridging relations to a main product, competing products, and interacting products. Current approaches to aspect based sentiment analysis (ABSA) and opinion summarization largely ignore this complexity. On the other hand, existing systems for coreference and bridging were trained in a different domain. We collect mention type annotations relevant to coreference and bridging for 498 product reviews. Using these annotations, we show that a state-of-the-art factuality score fails to catch coreference errors in product reviews, and that a state-of-the-art coreference system trained on OntoNotes does not perform nearly as well on product mentions. As our dataset grows, we expect it to help ABSA and opinion summarization systems to avoid entity reference errors.

Analyzing Coreference and Bridging in Product Reviews

We present a first release of 500 documents from the multimodal corpus Tell-me-more (Ilinykh et al., 2019) annotated with coreference information according to the ARRAU guidelines (Poesio et al., 2021). The corpus consists of images and short texts of five sentences. We describe the annotation process and present the adaptations to the original guidelines in order to account for the challenges of grounding the annotations to the image. 50 documents from the 500 available are annotated by two people and used to estimate inter-annotator agreement (IAA) relying on Krippendorff’s α.

Anaphoric Phenomena in Situated dialog: A First Round of Annotations

This paper presents the complete workflow of building a manually annotated Hungarian corpus, KorKor, with particular reference to anaphora and coreference annotation. All linguistic annotation layers were corrected manually. The corpus is freely available in two formats. The paper gives insight into the process of setting up the workflow and the challenges that have arisen.

Building a Manually Annotated Hungarian Coreference Corpus: Workflow and Tools

We present the Norwegian Anaphora Resolution Corpus (NARC), the first publicly available corpus annotated with anaphoric relations between noun phrases for Norwegian. The paper describes the annotated data for 326 documents in Norwegian Bokmål, together with inter-annotator agreement and discussions of relevant statistics. We also present preliminary modelling results which are comparable to existing corpora for other languages, and discuss relevant problems in relation to both modelling and the annotations themselves.

NARC – Norwegian Anaphora Resolution Corpus

Coreference resolution is a key step in natural language understanding. Developments in coreference resolution are mainly focused on improving the performance on standard datasets annotated for coreference resolution. However, coreference resolution is an intermediate step for text understanding and it is not clear how these improvements translate into downstream task performance. In this paper, we perform a thorough investigation on the impact of coreference resolvers in multiple settings of community-based question answering task, i.e., answer selection with long answers. Our settings cover multiple text domains and encompass several answer selection methods. We first inspect extrinsic evaluation of coreference resolvers on answer selection by using coreference relations to decontextualize individual sentences of candidate answers, and then annotate a subset of answers with coreference information for intrinsic evaluation. The results of our extrinsic evaluation show that while there is a significant difference between the performance of the rule-based system vs. state-of-the-art neural model on coreference resolution datasets, we do not observe a considerable difference on their impact on downstream models. Our intrinsic evaluation shows that (i) resolving coreference relations on less-formal text genres is more difficult even for trained annotators, and (ii) the values of linguistic-agnostic coreference evaluation metrics do not correlate with the impact on downstream data.

Evaluating Coreference Resolvers on Community-based Question Answering: From Rule-based to State of the Art

Bridging reference resolution is a reference resolution task of finding non-identical antecedents. One challenge of this task is that continuous essentiality between an anaphor and its antecedent is not well-represented in existing datasets.
We propose a crowdsourcing-based annotation method to obtain continuous labels.
Experimental results showed that adding our constructed dataset improves the resolution performance.

Improving Bridging Reference Resolution using Continuous Essentiality from Crowdsourcing

In this paper we present baseline results for Event Coreference Resolution (ECR) in Dutch using gold-standard (i.e non-predicted) event mentions. A newly developed benchmark dataset allows us to properly investigate the possibility of creating ECR systems for both within and cross-document coreference. We give an overview of the state of the art for ECR in other languages, as well as a detailed overview of existing ECR resources. Afterwards, we provide a comparative report on our own dataset. We apply a significant number of approaches that have been shown to attain good results for English ECR including feature-based models, monolingual transformer language models and multilingual language models. The best results were obtained using the monolingual BERTje model. Finally, results for all models are thoroughly analysed and visualised, as to provide insight into the inner workings of ECR and long-distance semantic NLP tasks in general.

Investigating Cross-Document Event Coreference for Dutch

In this paper, we frame the problem of co-reference resolution in dialogue as a dynamic social process in which mentions to people previously known and newly introduced are mixed when people know each other well. We restructured an existing data set for the Friends sitcom as a coreference task that evolves over time, where close friends make reference to other people either part of their common ground (inner circle) or not (outer circle). We expect that awareness of common ground is key in social dialogue in order to resolve references to the inner social circle, whereas local contextual information plays a more important role for outer circle mentions. Our analysis of these references confirms that there are differences in naming and introducing these people. We also experimented with the SpanBERT coreference system with and without fine-tuning to measure whether preceding discourse contexts matter for resolving inner and outer circle mentions. Our results show that more inner circle mentions lead to a decrease in model performance, and that fine-tuning on preceding contexts reduces false negatives for both inner and outer circle mentions but increases the false positives as well, showing that the models overfit on these contexts.


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Finding Scientific Topics in Continuously Growing Text Corpora

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