7 citations · 26 across the 15 of their papers we have counts for
17 papers · 1 filter
Fanar 2.0: Arabic Generative AI Stack
FANAR TEAM, Ummar Abbas, Mohammad Shahmeer Ahmad +34
We present Fanar 2.0, the second generation of Qatar's Arabic-centric Generative AI platform. Sovereignty is a first-class design principle: every component, from data pipelines to…
Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Models
Sawsan Alqahtani, Mir Tafseer Nayeem, Md Tahmid Rahman Laskar +2
Tokenization underlies every large language model, yet it remains an under-theorized and inconsistently designed component. Common subword approaches such as Byte Pair Encoding (BP…
Ready to Translate, Not to Represent? Bias and Performance Gaps in Multilingual LLMs Across Language Families and Domains
Md. Faiyaz Abdullah Sayeedi, Md. Mahbub Alam, Subhey Sadi Rahman +5
The rise of Large Language Models (LLMs) has redefined Machine Translation (MT), enabling context-aware and fluent translations across hundreds of languages and textual domains. De…
Beyond Fertility: Analyzing STRR as a Metric for Multilingual Tokenization Evaluation
Mir Tafseer Nayeem, Sawsan Alqahtani, Md Tahmid Rahman Laskar +2
Tokenization is a crucial but under-evaluated step in large language models (LLMs). The standard metric, fertility (the average number of tokens per word), captures compression eff…
MathMist: A Parallel Multilingual Benchmark Dataset for Mathematical Problem Solving and Reasoning
Mahbub E Sobhani, Md. Faiyaz Abdullah Sayeedi, Tasnim Mohiuddin +2
Mathematical reasoning remains one of the most challenging domains for large language models (LLMs), requiring not only linguistic understanding but also structured logical deducti…
Fanar: An Arabic-Centric Multimodal Generative AI Platform
Fanar Team, Ummar Abbas, Mohammad Shahmeer Ahmad +39
We present Fanar, a platform for Arabic-centric multimodal generative AI systems, that supports language, speech and image generation tasks. At the heart of Fanar are Fanar Star an…
GenAI Content Detection Task 2: AI vs. Human -- Academic Essay Authenticity Challenge
Shammur Absar Chowdhury, Hind Almerekhi, Mucahid Kutlu +5
This paper presents a comprehensive overview of the first edition of the Academic Essay Authenticity Challenge, organized as part of the GenAI Content Detection shared tasks colloc…
DM-Codec: Distilling Multimodal Representations for Speech Tokenization
Md Mubtasim Ahasan, Md Fahim, Tasnim Mohiuddin +6
Recent advancements in speech-language models have yielded significant improvements in speech tokenization and synthesis. However, effectively mapping the complex, multidimensional…
Data Selection Curriculum for Neural Machine Translation
Tasnim Mohiuddin, Philipp Koehn, Vishrav Chaudhary +3
Neural Machine Translation (NMT) models are typically trained on heterogeneous data that are concatenated and randomly shuffled. However, not all of the training data are equally u…
AUGVIC: Exploiting BiText Vicinity for Low-Resource NMT
Tasnim Mohiuddin, M Saiful Bari, Shafiq Joty
The success of Neural Machine Translation (NMT) largely depends on the availability of large bitext training corpora. Due to the lack of such large corpora in low-resource language…
Rethinking Coherence Modeling: Synthetic vs. Downstream Tasks
Tasnim Mohiuddin, Prathyusha Jwalapuram, Xiang Lin +1
Although coherence modeling has come a long way in developing novel models, their evaluation on downstream applications for which they are purportedly developed has largely been ne…
LNMap: Departures from Isomorphic Assumption in Bilingual Lexicon Induction Through Non-Linear Mapping in Latent Space
Tasnim Mohiuddin, M Saiful Bari, Shafiq Joty
Most of the successful and predominant methods for bilingual lexicon induction (BLI) are mapping-based, where a linear mapping function is learned with the assumption that the word…
UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP
M Saiful Bari, Tasnim Mohiuddin, Shafiq Joty
Transfer learning has yielded state-of-the-art (SoTA) results in many supervised NLP tasks. However, annotated data for every target task in every target language is rare, especial…
A Unified Neural Coherence Model
Han Cheol Moon, Tasnim Mohiuddin, Shafiq Joty +1
Recently, neural approaches to coherence modeling have achieved state-of-the-art results in several evaluation tasks. However, we show that most of these models often fail on harde…
Revisiting Adversarial Autoencoder for Unsupervised Word Translation with Cycle Consistency and Improved Training
Tasnim Mohiuddin, Shafiq Joty
Adversarial training has shown impressive success in learning bilingual dictionary without any parallel data by mapping monolingual embeddings to a shared space. However, recent wo…
Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation
Tasnim Mohiuddin, Thanh-Tung Nguyen, Shafiq Joty
We address the problem of speech act recognition (SAR) in asynchronous conversations (forums, emails). Unlike synchronous conversations (e.g., meetings, phone), asynchronous domain…
Coherence Modeling of Asynchronous Conversations: A Neural Entity Grid Approach
Tasnim Mohiuddin, Shafiq Joty, Dat Tien Nguyen
We propose a novel coherence model for written asynchronous conversations (e.g., forums, emails), and show its applications in coherence assessment and thread reconstruction tasks.…