Korea, Republic of

Relation extraction models typically cast the problem of determining whether there is a relation between a pair of entities as a single decision. However, these models can struggle with long or complex language constructions in which two entities are not directly linked, as is often the case in scientific publications. We propose a novel approach that decomposes a binary relation into two unary relations that capture each argument&#39;s role in the relation separately. We create a stacked learning model that incorporates information from unary and binary relation extractors to determine whether a relation holds between two entities. We present experimental results showing that this approach outperforms several competitive relation extractors on a new corpus of planetary science publications as well as a benchmark dataset in the biology domain.

COLING 2022

Exploiting Unary Relations with Stacked Learning for Relation Extraction

stacked learning

relation extraction

information extraction

**Welcome to ACL-IJCNLP 2021!**

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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Relation extraction models typically cast the problem of determining whether there is a relation between a pair of entities as a single decision. However, these models can struggle with long or complex language constructions in which two entities are not directly linked, as is often the case in scientific publications. We propose a novel approach that decomposes a binary relation into two unary relations that capture each argument's role in the relation separately. We create a stacked learning model that incorporates information from unary and binary relation extractors to determine whether a relation holds between two entities. We present experimental results showing that this approach outperforms several competitive relation extractors on a new corpus of planetary science publications as well as a benchmark dataset in the biology domain.

workshop paper

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### 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.

We release a pretrained Japanese masked lan-
guage model for an academic domain. Pre-
trained masked language models have recently
improved the performance of various natural
language processing applications. In domains
such as medical and academic, which include
a lot of technical terms, domain-specific pre-
training is effective. While domain-specific
masked language models for medical and SNS
domains are widely used in Japanese, along
with domain-independent ones, pretrained mod-
els specific to the academic domain are not
publicly available. In this study, we pretrained
a RoBERTa-based Japanese masked language
model on paper abstracts from the academic
database CiNii Articles. Experimental results
on Japanese text classification in the academic
domain revealed the effectiveness of the pro-
posed model over existing pretrained models.

A Japanese Masked Language Model for Academic Domain

[Mask]ing named entities during pretraining helps BART to preserve named entities in a generated summary:
We address the named entity omission - the drawback of many current abstractive text summarizers. We suggest a custom pretraining objective to enhance the model’s attention on the named entities in a text.

Named Entity Inclusion in Abstractive Text Summarization

A scientific paper is traditionally prefaced by an abstract that summarizes the paper. Recently, research highlights that focus on the main findings of the paper have emerged as a complementary summary in addition to an abstract. However, highlights are not yet as common as abstracts, and are absent in many papers.
In this paper, we aim to automatically generate research highlights using different sections of a research paper as input. We investigate whether the use of named entity recognition on the input improves the quality of the generated highlights. In particular, we have used two deep learning-based models: the first is a pointer-generator network, and the second augments the first model with coverage mechanism. We then augment each of the above models with named entity recognition features. 
The proposed method can be used to produce highlights for papers with missing highlights. Our experiments show that adding named entity information improves the performance of the deep learning-based summarizers in terms of ROUGE, METEOR and BERTScore measures.

Named Entity Recognition Based Automatic Generation of Research Highlights

We address automatic citation sentence generation, which reduces the burden on writing scientific papers.
For highly accurate citation senetence generation, appropriate language must be learned using information such as the relationship between the cited source and the cited paper as well as the context in which the paper cited.
Although the abstracts of papers have been used for the generation in the past, they often contain extra information in the citation sentence, which might negatively impact the generation of citation sentences.
Therefore, this study attempts to learn a highly accurate citation sentence generation model using sentences from cited articles that resemble the previous sentence to the cited location, thereby utilizing information that is more useful for citation sentence generation.

Citation Sentence Generation Leveraging the Content of Cited Papers

The paper describes neural models developed for the DAGPap22 shared task hosted at the Third Workshop on Scholarly Document Processing. This shared task targets the automatic detection of generated scientific papers. Our work focuses on comparing different transformer-based models as well as using additional datasets and techniques to deal with imbalanced classes. As a final submission, we utilized an ensemble of SciBERT, RoBERTa, and DeBERTa fine-tuned using random oversampling technique. Our model achieved 99.24% in terms of F1-score. The official evaluation results have put our system at the third place.

Detecting generated scientific papers using an ensemble of transformer models

We present a new gold-standard dataset and a benchmark for the \textit{Research Theme Identification} task, a sub-task of the Scholarly Knowledge Graph Generation shared task, at the 3rd Workshop on Scholarly Document Processing. The objective of the shared task was to label given research papers with research themes from a total of 36 themes. The benchmark was compiled using data drawn from the
largest overall assessment of university research
output ever undertaken globally (the Research Excellence Framework - 2014).

We provide a performance comparison of a transformer-based ensemble, which obtains multiple predictions for a research paper, given its multiple textual fields (e.g. title, abstract, reference), with traditional machine learning models. The ensemble involves enriching the initial data with additional information from open-access digital libraries and Argumentative Zoning techniques. It uses a weighted sum aggregation for the multiple predictions to obtain a final single prediction for the given research paper.

Both data and the ensemble are publicly available on https://www.kaggle.com/competitions/sdp2022-scholarly-knowledge-graph-generation/data?select=task1_test_no_label.csv and https://github.com/ProjectDoSSIER/sdp2022, respectively.

Benchmark for Research Theme Classification of Scholarly Documents

This paper is a description of our participation in the Multi-document Summarization for Literature Review (MSLR) Shared Task, in which we explore summarization models to create an automatic review of scientific results. Rather than maximizing the metrics using expensive computational models, we placed ourselves in a situation of scarce computational resources and investigate the limits of a base sequence to sequence models (thus with a limited input length) to the task. Although we explore methods to feed the abstractive model with salient sentences only (using a first extractive step), we find that the results still need some improvements.

Exploring the limits of a base BART for multi-document summarization in the medical domain

Text summarization has been a trending domain of research in NLP in the past few decades. The medical domain is no exception to the same. Medical documents often contain a lot of jargon pertaining to certain domains, and performing an abstractive summarization on the same remains a challenge. This paper presents a summary of the findings that we obtained based on the shared task of Multidocument Summarization for Literature Review (MSLR). We stood fourth in the leaderboards for evaluation on the MS^2 and Cochrane datasets. We finetuned pre-trained models such as BART-large, DistilBART and T5-base on both these datasets. These models’ accuracy was later tested with a part of the same dataset using ROUGE scores as the evaluation metrics.

Abstractive Approaches To Multidocument Summarization Of Medical Literature Reviews

The MuP-2022 shared task focuses on multi-perspective scientific document summarization. Given a scientific document, with multiple reference summaries, our goal was to develop a model that can produce a generic summary covering as many aspects of the document as covered by all of its reference summaries. This paper describes our best official model, a fine-tuned BART large, along with a discussion on
the challenges of this task and some of our unofficial models including SOTA generation models. Our submitted model out performed the given, MuP 2022 shared task baselines on ROUGE-2, ROUGE-L and average ROUGE F1-scores. Code of our submission can be accessed here.

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Exploiting Unary Relations with Stacked Learning for Relation Extraction

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