Indonesia

Keeping track of all relevant recent publications and experimental results for a research area is a challenging task. Prior work has demonstrated the efficacy of information extraction models in various scientific areas. Recently, several datasets have been released for the yet understudied materials science domain. However, these datasets focus on sub-problems such as parsing synthesis procedures or on subdomains, e.g., solid oxide fuel cells. In this resource paper, we present MuLMS, a new dataset of 50 open-access articles, spanning seven sub-domains of materials science. The corpus has been annotated by domain experts with several layers ranging from named entities over relations to frame structures. We present competitive neural models for all tasks and demonstrate that multi-task training with existing related resources leads to benefits.

IJCNLP-AACL 2023

MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

workshop paper

### Welcome to IJCNLP-AACL 2023!
I am honored and happy to welcome you to IJCNLP-AACL 2023! The conference hosts both the 13th
International Joint Conference on Natural Language Processing (IJCNLP) and the 3rd Conference of
the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL).<br>
The joint conferences of IJCNLP and AACL have been organized by alternating leadership in the
Asia-Pacific region, in odd years by Asian Federation of Natural Language Processing (AFNLP) and
in even years by AACL. The conference is organized by AFNLP this year.<br>

Considering the current situation that we are not yet fully over the coronavirus pandemic, the
conference is offered in a hybrid mode, off-line in Nusa Dua, Bali, Indonesia, and on-line with the help
of Underline, during November 1-4, 2023. For those attending off-line, I hope that you will find time to
explore the exciting local area, which remains quite warm and pleasant at this time of the year. For
those attending on-line, I hope that the attendance is not much hampered, sometimes even enhanced
and comfortable, though the on-line arrangements have been made without ample time. 

Message from Jong C. Park, Korea Advanced Institute of Science and Technology (KAIST)<br>
IJCNLP-AACL 2023 General Chair<br><br>
###### [Conference Handbook](https://drive.google.com/file/d/1S2JSXa105xJxIkNBfhYmGH20Om6wARBx/view?usp=sharing)
<br><br><br><br>

In order to acces this event page, you need to register [here](http://www.ijcnlp-aacl2023.org/)

The 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics

Weak supervision has emerged as a promising approach for rapid and large-scale
dataset creation in response to the increasing demand for accelerated NLP development. By
leveraging labeling functions, weak supervision allows practitioners to generate datasets
quickly by creating learned label models that produce soft-labeled datasets. This paper aims to
show how such an approach can be utilized to build an Indonesian NLP dataset from
conservation news text. We construct two types of datasets: multi-class classification and
sentiment classification. We then provide baseline experiments using various pretrained
language models. These baseline results demonstrate test performances of 59.79% accuracy
and 55.72% F1-score for sentiment classification, 66.87% F1-score-macro, 71.5% F1-score-
micro, and 83.67% ROC-AUC for multi-class classification. Additionally, we release the datasets
and labeling functions used in this work for further research and exploration.

Utilizing Weak Supervision to Generate Indonesian Conservation Datasets

Large language models (LLMs) that are tuned with instructions have demonstrated
remarkable capabilities in various tasks and languages. However, their ability to generalize to
underrepresented languages is limited due to the scarcity of available data. Additionally,
directly adapting new languages to instruction-tuned LLMs can result in catastrophic forgetting,
which leads to the loss of multitasking ability. To address this issue, we propose InstructAlign
which uses continual crosslingual instruction tuning to enable LLMs to align new unseen
languages with previously learned high-resource languages. Our results demonstrate the
effectiveness of InstructAlign in enabling the model to understand low-resource languages with
limited parallel data while preventing catastrophic forgetting. Our work contributes to the
advancement of language adaptation methods, particularly for adapting instruction-tuned LLMs
to underrepresented languages. We will release our code publicly upon acceptance.

InstructAlign: High-and-Low Resource Language Alignment via Continual CrosslingualInstruction Tuning

Code-mixing is a common linguistic phenomenon when words or syllables from two or
more languages are used within a single sentence. Several works have been conducted on
building such datasets and performing downstream tasks on them. Although it is very common
to observe Code-Mixing with multiple languages, most datasets in this domain contain code-
mixed data from only two languages. In this paper, we introduce SentMix-3L, a novel test
dataset for the task of sentiment analysis which contains code-mixed data from 3 different
languages. We also provide a comprehensive experimental analysis with several models and
observe that zero-shot prompting with GPT-3.5 outperforms all the transformer-based models
on SentMix-3L.

SentMix-3L: A Novel Code-Mixed Test Dataset in Bangla-English-Hindi for SentimentAnalysis

Neural machine translation (NMT) for low-resource local languages in Indonesia faces
significant challenges, including the need for a representative benchmark and limited data
availability. This work addresses these challenges by comprehensively analyzing training NMT
systems for four low-resource local languages in Indonesia: Javanese, Sundanese,
Minangkabau, and Balinese. Our study encompasses various training approaches, paradigms,
data sizes, and a preliminary study into using large language models for synthetic low-resource
languages parallel data generation. We reveal specific trends and insights into practical
strategies for low-resource language translation. Our research demonstrates that despite
limited computational resources and textual data, several of our NMT systems achieve
competitive performances, rivaling the translation quality of zero-shot gpt-3.5-turbo. These
findings significantly advance NMT for low-resource languages, offering valuable guidance for
researchers in similar contexts.

Replicable Benchmarking of Neural Machine Translation (NMT) on Low-Resource Local Languages in Indonesia

We introduce a comprehensive end-to-end pipeline designed to extract complete bibliography section from English scientific articles in digital-born PDF format and further split them into individual citations. At the heart of our pipeline lies the utilization of Languageindependent Layout Transformer (LiLT), a multimodal model that combines text and layout features to enhance the accuracy and robustness of bibliography extraction. By considering both text and visual structure, LiLT significantly improves the identification of bibliographic sections within scientific articles. To split the extracted full bibliography into individual citations, we employ a custom fine-tuned version of SciBERT, a Transformer-based model that excels at handling complex formatting variations common in scholarly bibliography. Having such end-to-end pipeline in-house allows us to bypass reliance on third-party black box tools, such as GROBID, offering greater control and transparency in the bibliography extraction process. Another highlight of our pipeline is its extensibility, as it can be seamlessly adapted to multilingual and image-based PDFs, hence allowing its utility across a wide range of scholarly content. When evaluated on an in-house dataset of digital-born English PDF articles published at Elsevier, we achieved an F1-score of 94.6%, a notable 3.1% improvement over GROBID, which is a well-regarded tool for bibliography parsing in the industry.

An End-to-End Pipeline for Bibliography Extraction from Scientific Articles

Citation graphs represent the citation relations between papers, and they are commonly used by researchers to identify relevant papers. However, citation graphs do not always represent how papers are related to each other. To make more effective use of citation graphs to discover relevant papers, we can consider identifying the functions of citations and label each edge in the citation graphs with its function. This paper proposes a method to identify the functions of citations automatically. The proposed model utilizes language models, e.g., SciBERT, to identify the description of citation functions. However, the language models are limited in terms of the number of input tokens; thus, the entire citing paragraph cannot be processed at once. To overcome this problem, we analyzed the distribution of the descriptions of citation functions in the citing paragraphs and determined the focusing part in identifying the citation functions. Experiments conducted on scientific paper data demonstrated the effectiveness of the proposed method.

On the Use of Language Models for Function Identification of Citations in Scholarly Papers

Scientific research relies heavily on the exchange of knowledge through citations in academic literature. In the domain of astrophysics, the precise classification of citation functions and the extraction of contextual information are critical for understanding the vast universe of research papers. This paper presents the system description for the WIESP 2023 FOCAL shared task. We introduce an automated approach that leverages state-of-the-art language models, including ALBERT, RoBERTa, BERT, and DistillBERT, to classify citation functions and extract context within astrophysical paragraphs. Our system combines paraphrasing and question-answering techniques to achieve accurate results. Through comprehensive experiments, we demonstrate the robustness of our approach, with ALBERT consistently delivering strong performance.

Automated Citation Function Classification and Context Extraction in Astrophysics: Leveraging Paraphrasing and Question Answering

Measurement extraction is an information extraction subtask focused on extracting quantities and their dependent entities within a
given scientific text. Quantity extraction is
the first and most important step in measurement extraction. Most existing approaches
model the problem as a sequence-labeling task
using pre-trained language models (PLMs).
However, none of the existing systems have
utilised explicit syntactic knowledge to extend the PLM-based modeling. We propose a
syntax-enriched extension by integrating dependency tree representations as syntactic knowledge into transformer-based language models
to address the task of quantity extraction. We
apply our approach to a range of established
transformer-based models to evaluate our approach and analyze its impact in experiments
on scientific literature datasets. Our experimental results and in-depth analysis show that our
approach, syntax-enriched RoBERTa, outperforms the other models, even in situations with
scarce training data in the scientific domain.
The results demonstrate the adaptability of the
proposed model to the tasks, especially useful
in low-resource scenarios.

Investigating the Impact of Syntax-Enriched Transformers on Quantity Extraction in Scientific Texts

In this paper, we address the task of extracting semantic relations between entities in scientific articles in Russian, with a focus on scientific terms as entities. We present a dataset that includes annotated abstracts of scientific articles in Russian. This dataset was used to train and test models and develop an algorithm for the automatic extraction of semantic relations. We conducted experiments and compared one zero-shot and one few-shot approach for relation extraction: one based on the perplexity score and the other based on the use of prototype vectors of relations. Our results show that both methods can achieve reasonable performance, demonstrating the potential of zeroshot and few-shot approaches for relation extraction in scientific texts in Russian. The developed tool and annotated dataset are publicly available and could be valuable resources for other researchers.

Relation Extraction from Scientific Texts in Russian with Limited Training Data

We consider automatically identifying the defined term within a mathematical definition from the text of an academic article. Inspired by the development of transformer-based natural language processing applications, we pose the problem as (a) a token-level classification task using fine-tuned pre-trained transformers; and (b) a question-answering task using a generalist large language model (GPT). We also propose a rule-based approach to build a labeled dataset from the LATEX source of papers. Experimental results show that it is possible to reach high levels of precision and recall using either recent (and expensive) GPT 4 or simpler pre-trained models fine-tuned on our task.

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Utilizing Weak Supervision to Generate Indonesian Conservation Datasets

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MS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text

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Next from IJCNLP-AACL 2023

Utilizing Weak Supervision to Generate Indonesian Conservation Datasets

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MS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

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