29 citations · 84 across the 17 of their papers we have counts for
17 papers
Integral Transformer: Denoising Attention, Not Too Much Not Too Little
Ivan Kobyzev, Abbas Ghaddar, Dingtao Hu +1
Softmax self-attention often assigns disproportionate weight to semantically uninformative tokens such as special tokens and punctuation, a phenomenon known as attention noise. Whi…
EWEK-QA: Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems
Mohammad Dehghan, Mohammad Ali Alomrani, Sunyam Bagga +12
The emerging citation-based QA systems are gaining more attention especially in generative AI search applications. The importance of extracted knowledge provided to these systems i…
OTTAWA: Optimal TransporT Adaptive Word Aligner for Hallucination and Omission Translation Errors Detection
Chenyang Huang, Abbas Ghaddar, Ivan Kobyzev +3
Recently, there has been considerable attention on detecting hallucinations and omissions in Machine Translation (MT) systems. The two dominant approaches to tackle this task invol…
CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search
Fengran Mo, Abbas Ghaddar, Kelong Mao +4
In this paper, we study how open-source large language models (LLMs) can be effectively deployed for improving query rewriting in conversational search, especially for ambiguous qu…
CHARP: Conversation History AwaReness Probing for Knowledge-grounded Dialogue Systems
Abbas Ghaddar, David Alfonso-Hermelo, Philippe Langlais +3
In this work, we dive deep into one of the popular knowledge-grounded dialogue benchmarks that focus on faithfulness, FaithDial. We show that a significant portion of the FaithDial…
On the importance of Data Scale in Pretraining Arabic Language Models
Abbas Ghaddar, Philippe Langlais, Mehdi Rezagholizadeh +1
Pretraining monolingual language models have been proven to be vital for performance in Arabic Natural Language Processing (NLP) tasks. In this paper, we conduct a comprehensive st…
AraMUS: Pushing the Limits of Data and Model Scale for Arabic Natural Language Processing
Asaad Alghamdi, Xinyu Duan, Wei Jiang +9
Developing monolingual large Pre-trained Language Models (PLMs) is shown to be very successful in handling different tasks in Natural Language Processing (NLP). In this work, we pr…
Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Understanding
Abbas Ghaddar, Yimeng Wu, Sunyam Bagga +11
There is a growing body of work in recent years to develop pre-trained language models (PLMs) for the Arabic language. This work concerns addressing two major problems in existing…
CILDA: Contrastive Data Augmentation using Intermediate Layer Knowledge Distillation
Md Akmal Haidar, Mehdi Rezagholizadeh, Abbas Ghaddar +3
Knowledge distillation (KD) is an efficient framework for compressing large-scale pre-trained language models. Recent years have seen a surge of research aiming to improve KD by le…
JABER and SABER: Junior and Senior Arabic BERt
Abbas Ghaddar, Yimeng Wu, Ahmad Rashid +10
Language-specific pre-trained models have proven to be more accurate than multilingual ones in a monolingual evaluation setting, Arabic is no exception. However, we found that prev…
NATURE: Natural Auxiliary Text Utterances for Realistic Spoken Language Evaluation
David Alfonso-Hermelo, Ahmad Rashid, Abbas Ghaddar +2
Slot-filling and intent detection are the backbone of conversational agents such as voice assistants, and are active areas of research. Even though state-of-the-art techniques on p…
RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation
Md Akmal Haidar, Nithin Anchuri, Mehdi Rezagholizadeh +3
Intermediate layer knowledge distillation (KD) can improve the standard KD technique (which only targets the output of teacher and student models) especially over large pre-trained…
Knowledge Distillation with Noisy Labels for Natural Language Understanding
Shivendra Bhardwaj, Abbas Ghaddar, Ahmad Rashid +5
Knowledge Distillation (KD) is extensively used to compress and deploy large pre-trained language models on edge devices for real-world applications. However, one neglected area of…
End-to-End Self-Debiasing Framework for Robust NLU Training
Abbas Ghaddar, Philippe Langlais, Mehdi Rezagholizadeh +1
Existing Natural Language Understanding (NLU) models have been shown to incorporate dataset biases leading to strong performance on in-distribution (ID) test sets but poor performa…
Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition
Abbas Ghaddar, Philippe Langlais, Ahmad Rashid +1
In this work, we examine the ability of NER models to use contextual information when predicting the type of an ambiguous entity. We introduce NRB, a new testbed carefully designed…
Towards Zero-Shot Knowledge Distillation for Natural Language Processing
Ahmad Rashid, Vasileios Lioutas, Abbas Ghaddar +1
Knowledge Distillation (KD) is a common knowledge transfer algorithm used for model compression across a variety of deep learning based natural language processing (NLP) solutions.…
Robust Lexical Features for Improved Neural Network Named-Entity Recognition
Abbas Ghaddar, Philippe Langlais
Neural network approaches to Named-Entity Recognition reduce the need for carefully hand-crafted features. While some features do remain in state-of-the-art systems, lexical featur…