Search Results for author: Alham Fikri Aji

Found 66 papers, 27 papers with code

A Relation Extraction Dataset for Knowledge Extraction from Web Tables

1 code implementation COLING 2022 Siffi Singh, Alham Fikri Aji, Gaurav Singh, Christos Christodoulopoulos

Most datasets are constructed using synthetic tables that lack valuable metadata information, or are limited in size to be considered as a challenging evaluation set.

Knowledge Graphs Relation +1

The University of Edinburgh’s Bengali-Hindi Submissions to the WMT21 News Translation Task

1 code implementation WMT (EMNLP) 2021 Proyag Pal, Alham Fikri Aji, Pinzhen Chen, Sukanta Sen

We describe the University of Edinburgh’s Bengali\leftrightarrowHindi constrained systems submitted to the WMT21 News Translation task.

Translation

Towards better structured and less noisy Web data: Oscar with Register annotations

no code implementations COLING (WNUT) 2022 Veronika Laippala, Anna Salmela, Samuel Rönnqvist, Alham Fikri Aji, Li-Hsin Chang, Asma Dhifallah, Larissa Goulart, Henna Kortelainen, Marc Pàmies, Deise Prina Dutra, Valtteri Skantsi, Lintang Sutawika, Sampo Pyysalo

Web-crawled datasets are known to be noisy, as they feature a wide range of language use covering both user-generated and professionally edited content as well as noise originating from the crawling process.

Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning

no code implementations19 Apr 2024 Ahmed Elshabrawy, Yongxin Huang, Iryna Gurevych, Alham Fikri Aji

While Large Language Models (LLMs) exhibit remarkable capabilities in zero-shot and few-shot scenarios, they often require computationally prohibitive sizes.

Zero-shot Generalization

Daisy-TTS: Simulating Wider Spectrum of Emotions via Prosody Embedding Decomposition

no code implementations22 Feb 2024 Rendi Chevi, Alham Fikri Aji

This wide spectrum of emotions is well-studied in the structural model of emotions, which represents variety of emotions as derivative products of primary emotions with varying degrees of intensity.

Beyond Probabilities: Unveiling the Misalignment in Evaluating Large Language Models

no code implementations21 Feb 2024 Chenyang Lyu, Minghao Wu, Alham Fikri Aji

Large Language Models (LLMs) have demonstrated remarkable capabilities across various applications, fundamentally reshaping the landscape of natural language processing (NLP) research.

Multiple-choice

COPAL-ID: Indonesian Language Reasoning with Local Culture and Nuances

1 code implementation2 Nov 2023 Haryo Akbarianto Wibowo, Erland Hilman Fuadi, Made Nindyatama Nityasya, Radityo Eko Prasojo, Alham Fikri Aji

Unlike the previous Indonesian COPA dataset (XCOPA-ID), COPAL-ID incorporates Indonesian local and cultural nuances, and therefore, provides a more natural portrayal of day-to-day causal reasoning within the Indonesian cultural sphere.

Common Sense Reasoning

Low-Resource Clickbait Spoiling for Indonesian via Question Answering

no code implementations12 Oct 2023 Ni Putu Intan Maharani, Ayu Purwarianti, Alham Fikri Aji

Clickbait spoiling aims to generate a short text to satisfy the curiosity induced by a clickbait post.

Question Answering

QASiNa: Religious Domain Question Answering using Sirah Nabawiyah

1 code implementation12 Oct 2023 Muhammad Razif Rizqullah, Ayu Purwarianti, Alham Fikri Aji

This concludes Chat GPT is unsuitable for question answering task in religious domain especially for Islamic religion.

Large Language Model Question Answering +1

Style Over Substance: Evaluation Biases for Large Language Models

no code implementations6 Jul 2023 Minghao Wu, Alham Fikri Aji

This study investigates the behavior of crowd-sourced and expert annotators, as well as LLMs, when comparing outputs from different models.

Text Generation

Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering

no code implementations7 Jun 2023 Jinheon Baek, Alham Fikri Aji, Amir Saffari

We validate the performance of our KAPING framework on the knowledge graph question answering task, that aims to answer the user's question based on facts over a knowledge graph, on which ours outperforms relevant zero-shot baselines by up to 48% in average, across multiple LLMs of various sizes.

Graph Question Answering Language Modelling +1

Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation

1 code implementation24 May 2023 Haonan Li, Fajri Koto, Minghao Wu, Alham Fikri Aji, Timothy Baldwin

However, research on multilingual instruction tuning has been limited due to the scarcity of high-quality instruction-response datasets across different languages.

Instruction Following

WebIE: Faithful and Robust Information Extraction on the Web

no code implementations23 May 2023 Chenxi Whitehouse, Clara Vania, Alham Fikri Aji, Christos Christodoulopoulos, Andrea Pierleoni

We evaluate the in-domain, out-of-domain, and zero-shot cross-lingual performance of generative IE models and find models trained on WebIE show better generalisability.

Entity Linking

LLM-powered Data Augmentation for Enhanced Cross-lingual Performance

1 code implementation23 May 2023 Chenxi Whitehouse, Monojit Choudhury, Alham Fikri Aji

This paper explores the potential of leveraging Large Language Models (LLMs) for data augmentation in multilingual commonsense reasoning datasets where the available training data is extremely limited.

Data Augmentation

Multilingual Large Language Models Are Not (Yet) Code-Switchers

no code implementations23 May 2023 Ruochen Zhang, Samuel Cahyawijaya, Jan Christian Blaise Cruz, Genta Indra Winata, Alham Fikri Aji

Multilingual Large Language Models (LLMs) have recently shown great capabilities in a wide range of tasks, exhibiting state-of-the-art performance through zero-shot or few-shot prompting methods.

Benchmarking Language Identification +2

Direct Fact Retrieval from Knowledge Graphs without Entity Linking

no code implementations21 May 2023 Jinheon Baek, Alham Fikri Aji, Jens Lehmann, Sung Ju Hwang

There has been a surge of interest in utilizing Knowledge Graphs (KGs) for various natural language processing/understanding tasks.

Entity Disambiguation Entity Linking +5

A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models

no code implementations2 May 2023 Chenyang Lyu, Zefeng Du, Jitao Xu, Yitao Duan, Minghao Wu, Teresa Lynn, Alham Fikri Aji, Derek F. Wong, Siyou Liu, Longyue Wang

We conclude by emphasizing the critical role of LLMs in guiding the future evolution of MT and offer a roadmap for future exploration in the sector.

Document Translation Machine Translation +2

The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges

1 code implementation19 Dec 2022 Genta Indra Winata, Alham Fikri Aji, Zheng-Xin Yong, Thamar Solorio

Code-Switching, a common phenomenon in written text and conversation, has been studied over decades by the natural language processing (NLP) research community.

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

6 code implementations9 Nov 2022 BigScience Workshop, :, Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, Jonathan Tow, Alexander M. Rush, Stella Biderman, Albert Webson, Pawan Sasanka Ammanamanchi, Thomas Wang, Benoît Sagot, Niklas Muennighoff, Albert Villanova del Moral, Olatunji Ruwase, Rachel Bawden, Stas Bekman, Angelina McMillan-Major, Iz Beltagy, Huu Nguyen, Lucile Saulnier, Samson Tan, Pedro Ortiz Suarez, Victor Sanh, Hugo Laurençon, Yacine Jernite, Julien Launay, Margaret Mitchell, Colin Raffel, Aaron Gokaslan, Adi Simhi, Aitor Soroa, Alham Fikri Aji, Amit Alfassy, Anna Rogers, Ariel Kreisberg Nitzav, Canwen Xu, Chenghao Mou, Chris Emezue, Christopher Klamm, Colin Leong, Daniel van Strien, David Ifeoluwa Adelani, Dragomir Radev, Eduardo González Ponferrada, Efrat Levkovizh, Ethan Kim, Eyal Bar Natan, Francesco De Toni, Gérard Dupont, Germán Kruszewski, Giada Pistilli, Hady Elsahar, Hamza Benyamina, Hieu Tran, Ian Yu, Idris Abdulmumin, Isaac Johnson, Itziar Gonzalez-Dios, Javier de la Rosa, Jenny Chim, Jesse Dodge, Jian Zhu, Jonathan Chang, Jörg Frohberg, Joseph Tobing, Joydeep Bhattacharjee, Khalid Almubarak, Kimbo Chen, Kyle Lo, Leandro von Werra, Leon Weber, Long Phan, Loubna Ben allal, Ludovic Tanguy, Manan Dey, Manuel Romero Muñoz, Maraim Masoud, María Grandury, Mario Šaško, Max Huang, Maximin Coavoux, Mayank Singh, Mike Tian-Jian Jiang, Minh Chien Vu, Mohammad A. Jauhar, Mustafa Ghaleb, Nishant Subramani, Nora Kassner, Nurulaqilla Khamis, Olivier Nguyen, Omar Espejel, Ona de Gibert, Paulo Villegas, Peter Henderson, Pierre Colombo, Priscilla Amuok, Quentin Lhoest, Rheza Harliman, Rishi Bommasani, Roberto Luis López, Rui Ribeiro, Salomey Osei, Sampo Pyysalo, Sebastian Nagel, Shamik Bose, Shamsuddeen Hassan Muhammad, Shanya Sharma, Shayne Longpre, Somaieh Nikpoor, Stanislav Silberberg, Suhas Pai, Sydney Zink, Tiago Timponi Torrent, Timo Schick, Tristan Thrush, Valentin Danchev, Vassilina Nikoulina, Veronika Laippala, Violette Lepercq, Vrinda Prabhu, Zaid Alyafeai, Zeerak Talat, Arun Raja, Benjamin Heinzerling, Chenglei Si, Davut Emre Taşar, Elizabeth Salesky, Sabrina J. Mielke, Wilson Y. Lee, Abheesht Sharma, Andrea Santilli, Antoine Chaffin, Arnaud Stiegler, Debajyoti Datta, Eliza Szczechla, Gunjan Chhablani, Han Wang, Harshit Pandey, Hendrik Strobelt, Jason Alan Fries, Jos Rozen, Leo Gao, Lintang Sutawika, M Saiful Bari, Maged S. Al-shaibani, Matteo Manica, Nihal Nayak, Ryan Teehan, Samuel Albanie, Sheng Shen, Srulik Ben-David, Stephen H. Bach, Taewoon Kim, Tali Bers, Thibault Fevry, Trishala Neeraj, Urmish Thakker, Vikas Raunak, Xiangru Tang, Zheng-Xin Yong, Zhiqing Sun, Shaked Brody, Yallow Uri, Hadar Tojarieh, Adam Roberts, Hyung Won Chung, Jaesung Tae, Jason Phang, Ofir Press, Conglong Li, Deepak Narayanan, Hatim Bourfoune, Jared Casper, Jeff Rasley, Max Ryabinin, Mayank Mishra, Minjia Zhang, Mohammad Shoeybi, Myriam Peyrounette, Nicolas Patry, Nouamane Tazi, Omar Sanseviero, Patrick von Platen, Pierre Cornette, Pierre François Lavallée, Rémi Lacroix, Samyam Rajbhandari, Sanchit Gandhi, Shaden Smith, Stéphane Requena, Suraj Patil, Tim Dettmers, Ahmed Baruwa, Amanpreet Singh, Anastasia Cheveleva, Anne-Laure Ligozat, Arjun Subramonian, Aurélie Névéol, Charles Lovering, Dan Garrette, Deepak Tunuguntla, Ehud Reiter, Ekaterina Taktasheva, Ekaterina Voloshina, Eli Bogdanov, Genta Indra Winata, Hailey Schoelkopf, Jan-Christoph Kalo, Jekaterina Novikova, Jessica Zosa Forde, Jordan Clive, Jungo Kasai, Ken Kawamura, Liam Hazan, Marine Carpuat, Miruna Clinciu, Najoung Kim, Newton Cheng, Oleg Serikov, Omer Antverg, Oskar van der Wal, Rui Zhang, Ruochen Zhang, Sebastian Gehrmann, Shachar Mirkin, Shani Pais, Tatiana Shavrina, Thomas Scialom, Tian Yun, Tomasz Limisiewicz, Verena Rieser, Vitaly Protasov, Vladislav Mikhailov, Yada Pruksachatkun, Yonatan Belinkov, Zachary Bamberger, Zdeněk Kasner, Alice Rueda, Amanda Pestana, Amir Feizpour, Ammar Khan, Amy Faranak, Ana Santos, Anthony Hevia, Antigona Unldreaj, Arash Aghagol, Arezoo Abdollahi, Aycha Tammour, Azadeh HajiHosseini, Bahareh Behroozi, Benjamin Ajibade, Bharat Saxena, Carlos Muñoz Ferrandis, Daniel McDuff, Danish Contractor, David Lansky, Davis David, Douwe Kiela, Duong A. Nguyen, Edward Tan, Emi Baylor, Ezinwanne Ozoani, Fatima Mirza, Frankline Ononiwu, Habib Rezanejad, Hessie Jones, Indrani Bhattacharya, Irene Solaiman, Irina Sedenko, Isar Nejadgholi, Jesse Passmore, Josh Seltzer, Julio Bonis Sanz, Livia Dutra, Mairon Samagaio, Maraim Elbadri, Margot Mieskes, Marissa Gerchick, Martha Akinlolu, Michael McKenna, Mike Qiu, Muhammed Ghauri, Mykola Burynok, Nafis Abrar, Nazneen Rajani, Nour Elkott, Nour Fahmy, Olanrewaju Samuel, Ran An, Rasmus Kromann, Ryan Hao, Samira Alizadeh, Sarmad Shubber, Silas Wang, Sourav Roy, Sylvain Viguier, Thanh Le, Tobi Oyebade, Trieu Le, Yoyo Yang, Zach Nguyen, Abhinav Ramesh Kashyap, Alfredo Palasciano, Alison Callahan, Anima Shukla, Antonio Miranda-Escalada, Ayush Singh, Benjamin Beilharz, Bo wang, Caio Brito, Chenxi Zhou, Chirag Jain, Chuxin Xu, Clémentine Fourrier, Daniel León Periñán, Daniel Molano, Dian Yu, Enrique Manjavacas, Fabio Barth, Florian Fuhrimann, Gabriel Altay, Giyaseddin Bayrak, Gully Burns, Helena U. Vrabec, Imane Bello, Ishani Dash, Jihyun Kang, John Giorgi, Jonas Golde, Jose David Posada, Karthik Rangasai Sivaraman, Lokesh Bulchandani, Lu Liu, Luisa Shinzato, Madeleine Hahn de Bykhovetz, Maiko Takeuchi, Marc Pàmies, Maria A Castillo, Marianna Nezhurina, Mario Sänger, Matthias Samwald, Michael Cullan, Michael Weinberg, Michiel De Wolf, Mina Mihaljcic, Minna Liu, Moritz Freidank, Myungsun Kang, Natasha Seelam, Nathan Dahlberg, Nicholas Michio Broad, Nikolaus Muellner, Pascale Fung, Patrick Haller, Ramya Chandrasekhar, Renata Eisenberg, Robert Martin, Rodrigo Canalli, Rosaline Su, Ruisi Su, Samuel Cahyawijaya, Samuele Garda, Shlok S Deshmukh, Shubhanshu Mishra, Sid Kiblawi, Simon Ott, Sinee Sang-aroonsiri, Srishti Kumar, Stefan Schweter, Sushil Bharati, Tanmay Laud, Théo Gigant, Tomoya Kainuma, Wojciech Kusa, Yanis Labrak, Yash Shailesh Bajaj, Yash Venkatraman, Yifan Xu, Yingxin Xu, Yu Xu, Zhe Tan, Zhongli Xie, Zifan Ye, Mathilde Bras, Younes Belkada, Thomas Wolf

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.

Decoder Language Modelling +1

Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering

1 code implementation COLING 2022 Priyanka Sen, Alham Fikri Aji, Amir Saffari

We introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models.

Question Answering

ParaCotta: Synthetic Multilingual Paraphrase Corpora from the Most Diverse Translation Sample Pair

no code implementations PACLIC 2021 Alham Fikri Aji, Tirana Noor Fatyanosa, Radityo Eko Prasojo, Philip Arthur, Suci Fitriany, Salma Qonitah, Nadhifa Zulfa, Tomi Santoso, Mahendra Data

We release our synthetic parallel paraphrase corpus across 17 languages: Arabic, Catalan, Czech, German, English, Spanish, Estonian, French, Hindi, Indonesian, Italian, Dutch, Romanian, Russian, Swedish, Vietnamese, and Chinese.

Machine Translation Sentence +1

Nix-TTS: Lightweight and End-to-End Text-to-Speech via Module-wise Distillation

1 code implementation29 Mar 2022 Rendi Chevi, Radityo Eko Prasojo, Alham Fikri Aji, Andros Tjandra, Sakriani Sakti

We present Nix-TTS, a lightweight TTS achieved via knowledge distillation to a high-quality yet large-sized, non-autoregressive, and end-to-end (vocoder-free) TTS teacher model.

Decoder Knowledge Distillation +1

Which Student is Best? A Comprehensive Knowledge Distillation Exam for Task-Specific BERT Models

no code implementations3 Jan 2022 Made Nindyatama Nityasya, Haryo Akbarianto Wibowo, Rendi Chevi, Radityo Eko Prasojo, Alham Fikri Aji

We perform knowledge distillation (KD) benchmark from task-specific BERT-base teacher models to various student models: BiLSTM, CNN, BERT-Tiny, BERT-Mini, and BERT-Small.

Data Augmentation Knowledge Distillation +3

Synthetic Source Language Augmentation for Colloquial Neural Machine Translation

no code implementations30 Dec 2020 Asrul Sani Ariesandy, Mukhlis Amien, Alham Fikri Aji, Radityo Eko Prasojo

Neural machine translation (NMT) is typically domain-dependent and style-dependent, and it requires lots of training data.

Machine Translation NMT +1

Costs to Consider in Adopting NLP for Your Business

no code implementations16 Dec 2020 Made Nindyatama Nityasya, Haryo Akbarianto Wibowo, Radityo Eko Prasojo, Alham Fikri Aji

Recent advances in Natural Language Processing (NLP) have largely pushed deep transformer-based models as the go-to state-of-the-art technique without much regard to the production and utilization cost.

Compressing Neural Machine Translation Models with 4-bit Precision

no code implementations WS 2020 Alham Fikri Aji, Kenneth Heafield

We empirically show that NMT models based on Transformer or RNN architecture can be compressed up to 4-bit precision without any noticeable quality degradation.

Machine Translation NMT +2

Benchmarking Multidomain English-Indonesian Machine Translation

1 code implementation LREC 2020 Tri Wahyu Guntara, Alham Fikri Aji, Radityo Eko Prasojo

In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language, and therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic.

Benchmarking Machine Translation +2

From Research to Production and Back: Ludicrously Fast Neural Machine Translation

no code implementations WS 2019 Young Jin Kim, Marcin Junczys-Dowmunt, Hany Hassan, Alham Fikri Aji, Kenneth Heafield, Roman Grundkiewicz, Nikolay Bogoychev

Taking our dominating submissions to the previous edition of the shared task as a starting point, we develop improved teacher-student training via multi-agent dual-learning and noisy backward-forward translation for Transformer-based student models.

C++ code Decoder +2

Neural Machine Translation with 4-Bit Precision and Beyond

no code implementations13 Sep 2019 Alham Fikri Aji, Kenneth Heafield

We empirically show that NMT models based on Transformer or RNN architecture can be compressed up to 4-bit precision without any noticeable quality degradation.

Machine Translation NMT +2

Making Asynchronous Stochastic Gradient Descent Work for Transformers

no code implementations WS 2019 Alham Fikri Aji, Kenneth Heafield

Asynchronous stochastic gradient descent (SGD) is attractive from a speed perspective because workers do not wait for synchronization.

Machine Translation Translation

Toward a Standardized and More Accurate Indonesian Part-of-Speech Tagging

1 code implementation10 Sep 2018 Kemal Kurniawan, Alham Fikri Aji

Previous work in Indonesian part-of-speech (POS) tagging are hard to compare as they are not evaluated on a common dataset.

Part-Of-Speech Tagging POS +1

Accelerating Asynchronous Stochastic Gradient Descent for Neural Machine Translation

no code implementations EMNLP 2018 Nikolay Bogoychev, Marcin Junczys-Dowmunt, Kenneth Heafield, Alham Fikri Aji

In order to extract the best possible performance from asynchronous stochastic gradient descent one must increase the mini-batch size and scale the learning rate accordingly.

Machine Translation Translation

Marian: Fast Neural Machine Translation in C++

2 code implementations ACL 2018 Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, André F. T. Martins, Alexandra Birch

We present Marian, an efficient and self-contained Neural Machine Translation framework with an integrated automatic differentiation engine based on dynamic computation graphs.

Decoder Machine Translation +1

Sparse Communication for Distributed Gradient Descent

no code implementations EMNLP 2017 Alham Fikri Aji, Kenneth Heafield

Most configurations work on MNIST, whereas different configurations reduce convergence rate on the more complex translation task.

General Classification Image Classification +4

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