Indonesia

Transfer learning has led to large gains in performance for nearly all NLP tasks while making downstream models easier and faster to train. This has also been extended to low-resourced languages, with some success. We investigate the properties of cross-lingual transfer learning between ten low-resourced languages, from the perspective of a named entity recognition task. We specifically investigate how much adaptive fine-tuning and the choice of transfer language affect zero-shot transfer performance. We find that models that perform well on a single language often do so at the expense of generalising to others, while models with the best generalisation to other languages suffer in individual language performance. Furthermore, the amount of data overlap between the source and target datasets is a better predictor of transfer performance than either the geographical or genetic distance between the languages.

IJCNLP-AACL 2023

Analysing Cross-Lingual Transfer in Low-Resourced African Named Entity Recognition

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

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technical 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 
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The 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics

We present an approach for assessing how multilingual large language models (LLMs) learn syntax in terms of multi-formalism syntactic structures. We aim to recover constituent and dependency structures by casting parsing as sequence labeling. To do so, we select a few LLMs and study them on 13 diverse UD treebanks for dependency parsing and 10 treebanks for constituent parsing. Our results show that the framework is consistent across encodings, and notes the role played by factors such as the syntactic formalism, the LLM differences, and the pretraining and assessment data.

Assessment of Pre-Trained Models Across Languages and Grammars

The scarcity of comprehensive, high-quality Question-Answering (QA) datasets in low-resource languages has greatly limited the progress of research on QA for these languages. This has inspired research on Question-Answer Generation (QAG) which seeks to synthetically generate QA pairs and minimize the human effort required to compile labeled datasets. In this paper, we present the first QAG pipeline for the Bengali language, which consists of an answer span extraction model, a question generation model, and roundtrip consistency filtering to discard inconsistent QA pairs. To train our QAG pipeline, we translate SQuAD1.1 and SQuAD2.0 using the state-of-the-art NLLB machine translation model and accurately mark the answer spans using a novel embedding-based answer alignment algorithm to construct two Bengali QA datasets that we show are superior to the only two existing machine-translated datasets in terms of quality and quantity. We use our QAG pipeline to generate more than 170,000 QA pairs to build BanglaQA, a synthetic QA dataset from 16,000 Bengali news articles spanning 5 different news categories. We demonstrate the quality of BanglaQA by human evaluation on a variety of metrics. The best-performing model among several baselines on our dataset achieves an F1 score of 86.14 falling behind human performance of 95.72 F1. Our codebase and curated datasets are publicly available at https://github.com/shihabshahriar16/BengaliQAG.git.

Question Answer Generation in Bengali: Mitigating the scarcity of QA datasets in a low-resource language

The research for detecting offensive language on online platforms has much advanced. However, the majority of these studies have primarily focused on English. Given the unique characteristics of offensive language, where social and cultural contexts significantly influence content understanding, language-specific datasets are essential. Acquiring comprehensive datasets in Korean, a less-resourced language, has mostly relied on human annotations, suffering from inherent limitations in terms of labor intensity and potential annotator bias. Automatic generation of datasets using generative methods offers an alternative approach to address these limitations, yet faces challenges in capturing linguistic and cultural diversities while maintaining native-level fluency. To address these challenges, we introduce a prompt design methodology, Korean Offensive language Machine Generation (K-OMG), using large language models. By manipulating three prompt factors, we find an effective prompt design to generate culturally aligned offensive language with fluent expressions. Experimental results demonstrate the high quality and utility of our automatically generated dataset. Our detailed analysis shows that the proposed approach achieves exceptional fluency in generating texts while effectively incorporating social and cultural diversities.

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Assessment of Pre-Trained Models Across Languages and Grammars

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Next from IJCNLP-AACL 2023

Assessment of Pre-Trained Models Across Languages and Grammars

Similar lecture

MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

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Downloads