Ayu Purwarianti


2023

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Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
Jong C. Park | Yuki Arase | Baotian Hu | Wei Lu | Derry Wijaya | Ayu Purwarianti | Adila Alfa Krisnadhi
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

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NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages
Samuel Cahyawijaya | Holy Lovenia | Fajri Koto | Dea Adhista | Emmanuel Dave | Sarah Oktavianti | Salsabil Akbar | Jhonson Lee | Nuur Shadieq | Tjeng Wawan Cenggoro | Hanung Linuwih | Bryan Wilie | Galih Muridan | Genta Winata | David Moeljadi | Alham Fikri Aji | Ayu Purwarianti | Pascale Fung
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

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Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
Jong C. Park | Yuki Arase | Baotian Hu | Wei Lu | Derry Wijaya | Ayu Purwarianti | Adila Alfa Krisnadhi
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)

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NusaCrowd: Open Source Initiative for Indonesian NLP Resources
Samuel Cahyawijaya | Holy Lovenia | Alham Fikri Aji | Genta Winata | Bryan Wilie | Fajri Koto | Rahmad Mahendra | Christian Wibisono | Ade Romadhony | Karissa Vincentio | Jennifer Santoso | David Moeljadi | Cahya Wirawan | Frederikus Hudi | Muhammad Satrio Wicaksono | Ivan Parmonangan | Ika Alfina | Ilham Firdausi Putra | Samsul Rahmadani | Yulianti Oenang | Ali Septiandri | James Jaya | Kaustubh Dhole | Arie Suryani | Rifki Afina Putri | Dan Su | Keith Stevens | Made Nindyatama Nityasya | Muhammad Adilazuarda | Ryan Hadiwijaya | Ryandito Diandaru | Tiezheng Yu | Vito Ghifari | Wenliang Dai | Yan Xu | Dyah Damapuspita | Haryo Wibowo | Cuk Tho | Ichwanul Karo Karo | Tirana Fatyanosa | Ziwei Ji | Graham Neubig | Timothy Baldwin | Sebastian Ruder | Pascale Fung | Herry Sujaini | Sakriani Sakti | Ayu Purwarianti
Findings of the Association for Computational Linguistics: ACL 2023

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments.NusaCrowd’s data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia. Our work strives to advance natural language processing (NLP) research for languages that are under-represented despite being widely spoken.

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Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)
Jong C. Park | Yuki Arase | Baotian Hu | Wei Lu | Derry Wijaya | Ayu Purwarianti | Adila Alfa Krisnadhi
Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)

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Speech Recognition and Meaning Interpretation: Towards Disambiguation of Structurally Ambiguous Spoken Utterances in Indonesian
Ruhiyah Widiaputri | Ayu Purwarianti | Dessi Lestari | Kurniawati Azizah | Dipta Tanaya | Sakriani Sakti
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Despite being the world’s fourth-most populous country, the development of spoken language technologies in Indonesia still needs improvement. Most automatic speech recognition (ASR) systems that have been developed are still limited to transcribing the exact word-by-word, which, in many cases, consists of ambiguous sentences. In fact, speakers use prosodic characteristics of speech to convey different interpretations, which, unfortunately, these systems often ignore. In this study, we attempt to resolve structurally ambiguous utterances into unambiguous texts in Indonesian using prosodic information. To the best of our knowledge, this might be the first study to address this problem in the ASR context. Our contributions include (1) collecting the Indonesian speech corpus on structurally ambiguous sentences; (2) conducting a survey on how people disambiguate structurally ambiguous sentences presented in both text and speech forms; and (3) constructing an Indonesian ASR and meaning interpretation system by utilizing both cascade and direct approaches to map speech to text, along with two additional prosodic information signals (pause and pitch). The experimental results reveal that it is possible to disambiguate these utterances. In this study, the proposed cascade system, utilizing Mel-spectrograms concatenated with F0 and energy as input, achieved a disambiguation accuracy of 79.6%, while the proposed direct system with the same input yielded an even more impressive disambiguation accuracy of 82.2%.

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ICON: Building a Large-Scale Benchmark Constituency Treebank for the Indonesian Language
Ee Suan Lim | Wei Qi Leong | Ngan Thanh Nguyen | Dea Adhista | Wei Ming Kng | William Chandra Tjh | Ayu Purwarianti
Proceedings of the 21st International Workshop on Treebanks and Linguistic Theories (TLT, GURT/SyntaxFest 2023)

Constituency parsing is an important task of informing how words are combined to form sentences. While constituency parsing in English has seen significant progress in the last few years, tools for constituency parsing in Indonesian remain few and far between. In this work, we publish ICON (Indonesian CONstituency treebank), the hitherto largest publicly-available manually-annotated benchmark constituency treebank for the Indonesian language with a size of 10,000 sentences and approximately 124,000 constituents and 182,000 tokens, which can support the training of state-of-the-art transformer-based models. We establish strong baselines on the ICON dataset using the Berkeley Neural Parser with transformer-based pre-trained embeddings, with the best performance of 88.85% F1 score coming from our own version of SpanBERT (IndoSpanBERT). We further analyze the predictions made by our best-performing model to reveal certain idiosyncrasies in the Indonesian language that pose challenges for constituency parsing.

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Proceedings of the First Workshop in South East Asian Language Processing
Derry Wijaya | Alham Fikri Aji | Clara Vania | Genta Indra Winata | Ayu Purwarianti
Proceedings of the First Workshop in South East Asian Language Processing

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IndoToD: A Multi-Domain Indonesian Benchmark For End-to-End Task-Oriented Dialogue Systems
Muhammad Kautsar | Rahmah Nurdini | Samuel Cahyawijaya | Genta Winata | Ayu Purwarianti
Proceedings of the First Workshop in South East Asian Language Processing

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Replicable Benchmarking of Neural Machine Translation (NMT) on Low-Resource Local Languages in Indonesia
Lucky Susanto | Ryandito Diandaru | Adila Krisnadhi | Ayu Purwarianti | Derry Tanti Wijaya
Proceedings of the First Workshop in South East Asian Language Processing

2022

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IndoRobusta: Towards Robustness Against Diverse Code-Mixed Indonesian Local Languages
Muhammad Farid Adilazuarda | Samuel Cahyawijaya | Genta Indra Winata | Pascale Fung | Ayu Purwarianti
Proceedings of the First Workshop on Scaling Up Multilingual Evaluation

2021

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IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation
Samuel Cahyawijaya | Genta Indra Winata | Bryan Wilie | Karissa Vincentio | Xiaohong Li | Adhiguna Kuncoro | Sebastian Ruder | Zhi Yuan Lim | Syafri Bahar | Masayu Khodra | Ayu Purwarianti | Pascale Fung
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems. Unfortunately, the lack of publicly available NLG benchmarks for low-resource languages poses a challenging barrier for building NLG systems that work well for languages with limited amounts of data. Here we introduce IndoNLG, the first benchmark to measure natural language generation (NLG) progress in three low-resource—yet widely spoken—languages of Indonesia: Indonesian, Javanese, and Sundanese. Altogether, these languages are spoken by more than 100 million native speakers, and hence constitute an important use case of NLG systems today. Concretely, IndoNLG covers six tasks: summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks. We collate a clean pretraining corpus of Indonesian, Sundanese, and Javanese datasets, Indo4B-Plus, which is used to pretrain our models: IndoBART and IndoGPT. We show that IndoBART and IndoGPT achieve competitive performance on all tasks—despite using only one-fifth the parameters of a larger multilingual model, mBART-large (Liu et al., 2020). This finding emphasizes the importance of pretraining on closely related, localized languages to achieve more efficient learning and faster inference at very low-resource languages like Javanese and Sundanese.

2020

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IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding
Bryan Wilie | Karissa Vincentio | Genta Indra Winata | Samuel Cahyawijaya | Xiaohong Li | Zhi Yuan Lim | Sidik Soleman | Rahmad Mahendra | Pascale Fung | Syafri Bahar | Ayu Purwarianti
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Although Indonesian is known to be the fourth most frequently used language over the internet, the research progress on this language in natural language processing (NLP) is slow-moving due to a lack of available resources. In response, we introduce the first-ever vast resource for training, evaluation, and benchmarking on Indonesian natural language understanding (IndoNLU) tasks. IndoNLU includes twelve tasks, ranging from single sentence classification to pair-sentences sequence labeling with different levels of complexity. The datasets for the tasks lie in different domains and styles to ensure task diversity. We also provide a set of Indonesian pre-trained models (IndoBERT) trained from a large and clean Indonesian dataset (Indo4B) collected from publicly available sources such as social media texts, blogs, news, and websites. We release baseline models for all twelve tasks, as well as the framework for benchmark evaluation, thus enabling everyone to benchmark their system performances.

2017

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Ensemble Technique Utilization for Indonesian Dependency Parser
Arief Rahman | Ayu Purwarianti
Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation

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Rule-based Reordering and Post-Processing for Indonesian-Korean Statistical Machine Translation
Candy Olivia Mawalim | Dessi Puji Lestari | Ayu Purwarianti
Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation

2011

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Study and Implementation of Monolingual Approach on Indonesian Question Answering for Factoid and Non-Factoid Question
Alvin Andhika Zulen | Ayu Purwarianti
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation

2007

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Expanding Indonesian-Japanese Small Translation Dictionary Using a Pivot Language
Masatoshi Tsuchiya | Ayu Purwarianti | Toshiyuki Wakita | Seiichi Nakagawa
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions

2006

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Indonesian-Japanese CLIR Using Only Limited Resource
Ayu Purwarianti | Masatoshi Tsuchiya | Seiichi Nakagawa
Proceedings of the Workshop on How Can Computational Linguistics Improve Information Retrieval?

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