Indonesia

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.

IJCNLP-AACL 2023

Relation Extraction from Scientific Texts in Russian with Limited Training Data

[![](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!

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

COLING 2022

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

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). 
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. 

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) 
IJCNLP-AACL 2023 General Chair 
###### [Conference Handbook](https://drive.google.com/file/d/1S2JSXa105xJxIkNBfhYmGH20Om6wARBx/view?usp=sharing)

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 present the development of a Named Entity Recognition (NER) dataset for
Tagalog. This corpus helps fill the resource gap present in Philippine languages today, where
NER resources are scarce. The texts were obtained from a pretraining corpora containing news
reports, and were labeled by native speakers in an iterative fashion. The resulting dataset
contains ~7.8k documents across three entity types: Person, Organization, and Location. The
inter-annotator agreement, as measured by Cohen&#39;s Kappa, is 0.81.We also conducted
extensive empirical evaluation of state-of-the-art methods across supervised and transfer
learning settings. Finally, we released the data and processing code publicly to inspire future
work on Tagalog NLP.

Developing a Named Entity Recognition Dataset for Tagalog

Wordnets are indispensable tools for various natural language processing
applications. Unfortunately, wordnets get outdated, and producing or updating wordnets can
be slow and costly in terms of time and resources. This problem intensifies for low-resource
languages. This study proposes a method for word sense induction and synset induction using
only two linguistic resources, namely, an unlabeled corpus and a sentence embeddings-based
language model. The resulting sense inventory and synonym sets can be used in automatically
creating a wordnet. We applied this method on a corpus of Filipino text. The sense inventory
and synsets were evaluated by matching them with the sense inventory of the machine
translated Princeton WordNet, as well as comparing the synsets to the Filipino WordNet. This
study empirically shows that the 30% of the induced senses are valid and 40% of induced
synsets are valid in which 20% are novel synsets.

Towards Automatic Construction of Filipino WordNet: Word Sense Induction and Synset Induction Using Sentence Embeddings

Clickbait spoiling aims to generate a short text to satisfy the curiosity induced by a
clickbait post. As it is a newly introduced task, the dataset is only available in English so far. Our
contributions include the construction of manually labeled clickbait spoiling corpus in
Indonesian and an evaluation on using cross-lingual zero-shot question answering-based
models to tackle clickbait spoiling for low-resource language like Indonesian. We utilize
selection of multilingual language models. The experimental results suggest that XLM-RoBERTa
(large) model outperforms other models for phrase and passage spoilers, meanwhile,
mDeBERTa (base) model outperforms other models for multipart spoilers.

Low-Resource Clickbait Spoiling for Indonesian via Question Answering

While there are many grammar checkers available for various languages, especially
the English language, those that exist for the low resource Filipino language can only effectively
correct lexical errors. There is yet to be a publicly available Filipino grammar checker that can
also address semantic errors, which are more complex. As such, this study found an
opportunity to introduce Balarila, a deep learning-based Filipino GEC model inspired by the
GECToR approach. To address the absence of a training and test dataset, an automated error
generation pipeline was devised, creating synthetic datasets of error-free and error-filled
Filipino sentences sourced from various online news sources. Tagalog BERT and RoBERTa
models were fine-tuned in two stages using this generated corpus. Evaluation metrics included
precision, recall, and F0.5 scores for GEC, and a multi-class confusion matrix for GED. The top-
performing model, RoBERTa Tagalog Large, achieved an F0.5 score of 70.75, while the RoBERTa
Tagalog Base, with a F0.5 score of 69.00, demonstrated cost-effectiveness in training. The
created datasets can also be used as a benchmark for Filipino grammar checker models

Balarila: Deep Learning for Semantic Grammar Error Correction in Low-Resource Settings

Fluency is a crucial goal of all Natural Language Generation (NLG) systems. Widely
used automatic evaluation metrics fall short in capturing the fluency of machine-generated
text. Assessing the fluency of NLG systems poses a challenge since these models are not limited
to simply reusing words from the input but may also generate abstractions. Existing reference-
based fluency evaluations, such as word overlap measures, often exhibit weak correlations with
human judgments. This paper presents an unsupervised technique for measuring text fluency
without the need for any reference. Our approach leverages various word embeddings and
trains language models using Recurrent Neural Network (RNN) architectures. We also

experiment with other available multilingual Language Models (LMs). To assess the
performance of the models, we conduct a comparative analysis across 10 Indic languages,
correlating the obtained fluency scores with human judgments. We also release a human-
annotated benchmark test set for fluency.

Unsupervised approach to evaluate sentence-level fluency: Do we really need reference? 

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.

Downloads

Next from IJCNLP-AACL 2023

Utilizing Weak Supervision to Generate Indonesian Conservation Datasets

Similar lecture

TERMinator: A system for scientific texts processing

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from IJCNLP-AACL 2023

Utilizing Weak Supervision to Generate Indonesian Conservation Datasets

Similar lecture

TERMinator: A system for scientific texts processing

Downloads