MBZUAI researchers had 26 papers accepted at ACL 2023, a top NLP conference. Assistant Professor Alham Fikri Aji co-authored eight papers, including one on crosslingual generalization through multitask finetuning (MTF). Deputy Department Chair Preslav Nakov co-authored a paper on a Bulgarian language understanding benchmark dedicated to the memory of Yale Computer Scientist Dragomir R. Radev. Why it matters: MBZUAI's strong presence at ACL highlights its growing influence in the NLP field and its contributions to multilingual AI research.
MBZUAI Professor Timothy Baldwin delivered the presidential keynote at the 60th Annual Meeting of the Association for Computational Linguistics (ACL). Baldwin also published three papers at the conference, including work on biomedical literature summarization, NLP for Indonesian languages, and understanding procedural texts. The papers address challenges such as reducing human effort in reviewing medical documents and digitally preserving Indonesian indigenous languages. Why it matters: Baldwin's contributions and leadership role at ACL highlight the growing prominence of MBZUAI and GCC-based researchers in the global NLP community.
The first Workshop on Language Models for Low-Resource Languages (LoResLM 2025) was held in Abu Dhabi as part of COLING 2025. It provided a forum for researchers to share work on language models for low-resource languages. The workshop accepted 35 papers from 52 submissions, covering diverse languages and research areas.
MBZUAI faculty Alham Fikri Aji, Timothy Baldwin, and Fajri Koto won an Outstanding Paper Award at EACL 2023 for their paper "NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages." The paper introduces the first parallel resource for 10 Indonesian low-resource languages to boost performance in sentiment analysis and machine translation. The dataset is available on HuggingFace. Why it matters: This work highlights MBZUAI's commitment to advancing NLP research in low-resource languages, which can help preserve linguistic diversity and improve access to digital resources for speakers of underrepresented languages.
The paper introduces AlcLaM, an Arabic dialectal language model trained on 3.4M sentences from social media. AlcLaM expands the vocabulary and retrains a BERT-based model, using only 13GB of dialectal text. Despite the smaller training data, AlcLaM outperforms models like CAMeL, MARBERT, and ArBERT on various Arabic NLP tasks. Why it matters: AlcLaM offers a more efficient and accurate approach to Arabic NLP by focusing on dialectal Arabic, which is often underrepresented in existing models.
A talk will present two projects related to the use of NLP for estimating a client’s depression severity and well-being. The first project examines emotional coherence between the subjective experience of emotions and emotion expression in therapy using transformer-based emotion recognition models. The second project proposes a semantic pipeline to study depression severity in individuals based on their social media posts by exploring different aggregation methods to answer one of four Beck Depression Inventory (BDI) options per symptom. Why it matters: This research explores how NLP techniques can be applied to mental health assessment, potentially offering new tools for diagnosis and treatment monitoring.
This paper presents a benchmark study of contrastive learning (CL) methods applied to Arabic social meaning tasks like sentiment analysis and dialect identification. The study compares state-of-the-art supervised CL techniques against vanilla fine-tuning across a range of tasks. Results indicate that CL methods outperform vanilla fine-tuning in most cases and demonstrate data efficiency. Why it matters: This work highlights the potential of contrastive learning for improving performance in Arabic NLP, especially in low-resource scenarios.
MBZUAI had 22 papers accepted at ICLR 2023, with faculty Kun Zhang co-authoring seven of them. Yuanzhi Li, an affiliated assistant professor at MBZUAI, received an honorable mention for his paper on knowledge distillation. Additionally, a paper co-authored by MBZUAI President Eric Xing was recognized as a top 5% paper at the conference. Why it matters: MBZUAI's strong presence at a top-tier machine learning conference like ICLR demonstrates the university's growing influence and research capabilities in the global AI landscape.