Md Mahfuz Ibn Alam


2024

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CODET: A Benchmark for Contrastive Dialectal Evaluation of Machine Translation
Md Mahfuz Ibn Alam | Sina Ahmadi | Antonios Anastasopoulos
Findings of the Association for Computational Linguistics: EACL 2024

Neural machine translation (NMT) systems exhibit limited robustness in handling source-side linguistic variations. Their performance tends to degrade when faced with even slight deviations in language usage, such as different domains or variations introduced by second-language speakers. It is intuitive to extend this observation to encompass dialectal variations as well, but the work allowing the community to evaluate MT systems on this dimension is limited. To alleviate this issue, we compile and release CODET, a contrastive dialectal benchmark encompassing 891 different variations from twelve different languages. We also quantitatively demonstrate the challenges large MT models face in effectively translating dialectal variants. All the data and code have been released.

2023

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LIMIT: Language Identification, Misidentification, and Translation using Hierarchical Models in 350+ Languages
Milind Agarwal | Md Mahfuz Ibn Alam | Antonios Anastasopoulos
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Knowing the language of an input text/audio is a necessary first step for using almost every NLP tool such as taggers, parsers, or translation systems. Language identification is a well-studied problem, sometimes even considered solved; in reality, due to lack of data and computational challenges, current systems cannot accurately identify most of the world’s 7000 languages. To tackle this bottleneck, we first compile a corpus, MCS-350, of 50K multilingual and parallel children’s stories in 350+ languages. MCS-350 can serve as a benchmark for language identification of short texts and for 1400+ new translation directions in low-resource Indian and African languages. Second, we propose a novel misprediction-resolution hierarchical model, LIMIT, for language identification that reduces error by 55% (from 0.71 to 0.32) on our compiled children’s stories dataset and by 40% (from 0.23 to 0.14) on the FLORES-200 benchmark. Our method can expand language identification coverage into low-resource languages by relying solely on systemic misprediction patterns, bypassing the need to retrain large models from scratch.

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GMNLP at SemEval-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapters
Md Mahfuz Ibn Alam | Ruoyu Xie | Fahim Faisal | Antonios Anastasopoulos
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

This report describes GMU’s sentiment analysis system for the SemEval-2023 shared task AfriSenti-SemEval. We participated in all three sub-tasks: Monolingual, Multilingual, and Zero-Shot. Our approach uses models initialized with AfroXLMR-large, a pre-trained multilingual language model trained on African languages and fine-tuned correspondingly. We also introduce augmented training data along with original training data. Alongside finetuning, we perform phylogeny-based adapter-tuning to create several models and ensemble the best models for the final submission. Our system achieves the best F1-score on track 5: Amharic, with 6.2 points higher F1-score than the second-best performing system on this track. Overall, our system ranks 5th among the 10 systems participating in all 15 tracks.

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BIG-C: a Multimodal Multi-Purpose Dataset for Bemba
Claytone Sikasote | Eunice Mukonde | Md Mahfuz Ibn Alam | Antonios Anastasopoulos
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present BIG-C (Bemba Image Grounded Conversations), a large multimodal dataset for Bemba. While Bemba is the most populous language of Zambia, it exhibits a dearth of resources which render the development of language technologies or language processing research almost impossible. The dataset is comprised of multi-turn dialogues between Bemba speakers based on images, transcribed and translated into English. There are more than 92,000 utterances/sentences, amounting to more than 180 hours of audio data with corresponding transcriptions and English translations. We also provide baselines on speech recognition (ASR), machine translation (MT) and speech translation (ST) tasks, and sketch out other potential future multimodal uses of our dataset. We hope that by making the dataset available to the research community, this work will foster research and encourage collaboration across the language, speech, and vision communities especially for languages outside the “traditionally” used high-resourced ones. All data and code are publicly available: [https://github.com/csikasote/bigc](https://github.com/csikasote/bigc).

2022

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Findings of the WMT’22 Shared Task on Large-Scale Machine Translation Evaluation for African Languages
David Adelani | Md Mahfuz Ibn Alam | Antonios Anastasopoulos | Akshita Bhagia | Marta R. Costa-jussà | Jesse Dodge | Fahim Faisal | Christian Federmann | Natalia Fedorova | Francisco Guzmán | Sergey Koshelev | Jean Maillard | Vukosi Marivate | Jonathan Mbuya | Alexandre Mourachko | Safiyyah Saleem | Holger Schwenk | Guillaume Wenzek
Proceedings of the Seventh Conference on Machine Translation (WMT)

We present the results of the WMT’22 SharedTask on Large-Scale Machine Translation Evaluation for African Languages. The shared taskincluded both a data and a systems track, alongwith additional innovations, such as a focus onAfrican languages and extensive human evaluation of submitted systems. We received 14system submissions from 8 teams, as well as6 data track contributions. We report a largeprogress in the quality of translation for Africanlanguages since the last iteration of this sharedtask: there is an increase of about 7.5 BLEUpoints across 72 language pairs, and the average BLEU scores went from 15.09 to 22.60.

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Language Adapters for Large-Scale MT: The GMU System for the WMT 2022 Large-Scale Machine Translation Evaluation for African Languages Shared Task
Md Mahfuz Ibn Alam | Antonios Anastasopoulos
Proceedings of the Seventh Conference on Machine Translation (WMT)

This report describes GMU’s machine translation systems for the WMT22 shared task on large-scale machine translation evaluation for African languages. We participated in the constrained translation track where only the data listed on the shared task page were allowed, including submissions accepted to the Data track. Our approach uses models initialized with DeltaLM, a generic pre-trained multilingual encoder-decoder model, and fine-tuned correspondingly with the allowed data sources. Our best submission incorporates language family and language-specific adapter units; ranking ranked second under the constrained setting.

2021

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Findings of the WMT Shared Task on Machine Translation Using Terminologies
Md Mahfuz Ibn Alam | Ivana Kvapilíková | Antonios Anastasopoulos | Laurent Besacier | Georgiana Dinu | Marcello Federico | Matthias Gallé | Kweonwoo Jung | Philipp Koehn | Vassilina Nikoulina
Proceedings of the Sixth Conference on Machine Translation

Language domains that require very careful use of terminology are abundant and reflect a significant part of the translation industry. In this work we introduce a benchmark for evaluating the quality and consistency of terminology translation, focusing on the medical (and COVID-19 specifically) domain for five language pairs: English to French, Chinese, Russian, and Korean, as well as Czech to German. We report the descriptions and results of the participating systems, commenting on the need for further research efforts towards both more adequate handling of terminologies as well as towards a proper formulation and evaluation of the task.

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SD-QA: Spoken Dialectal Question Answering for the Real World
Fahim Faisal | Sharlina Keshava | Md Mahfuz Ibn Alam | Antonios Anastasopoulos
Findings of the Association for Computational Linguistics: EMNLP 2021

Question answering (QA) systems are now available through numerous commercial applications for a wide variety of domains, serving millions of users that interact with them via speech interfaces. However, current benchmarks in QA research do not account for the errors that speech recognition models might introduce, nor do they consider the language variations (dialects) of the users. To address this gap, we augment an existing QA dataset to construct a multi-dialect, spoken QA benchmark on five languages (Arabic, Bengali, English, Kiswahili, Korean) with more than 68k audio prompts in 24 dialects from 255 speakers. We provide baseline results showcasing the real-world performance of QA systems and analyze the effect of language variety and other sensitive speaker attributes on downstream performance. Last, we study the fairness of the ASR and QA models with respect to the underlying user populations.

2020

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Fine-Tuning MT systems for Robustness to Second-Language Speaker Variations
Md Mahfuz Ibn Alam | Antonios Anastasopoulos
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)

The performance of neural machine translation (NMT) systems only trained on a single language variant degrades when confronted with even slightly different language variations. With this work, we build upon previous work to explore how to mitigate this issue. We show that fine-tuning using naturally occurring noise along with pseudo-references (i.e. “corrected” non-native inputs translated using the baseline NMT system) is a promising solution towards systems robust to such type of input variations. We focus on four translation pairs, from English to Spanish, Italian, French, and Portuguese, with our system achieving improvements of up to 3.1 BLEU points compared to the baselines, establishing a new state-of-the-art on the JFLEG-ES dataset. All datasets and code are publicly available here: https://github.com/mahfuzibnalam/finetuning_for_robustness .