Nadir Durrani

The Operation Sequence Model—Combining N-Gram-Based and Phrase-Based Statistical Machine Translation

2015 · Computational Linguistics

In this article, we present a novel machine translation model, the Operation Sequence Model (OSM), which combines the benefits of phrase-based and N-gram-based statistical machine translation (SMT) and remedies their drawbacks. The model represents the …

Effect of Post-processing on Contextualized Word Representations

2021 · arXiv (Cornell University)

Post-processing of static embedding has beenshown to improve their performance on both lexical and sequence-level tasks. However, post-processing for contextualized embeddings is an under-studied problem. In this work, we question the usefulness of post-processing for …

Incremental Decoding and Training Methods for Simultaneous Translation\n in Neural Machine Translation

2018 · arXiv (Cornell University)

We address the problem of simultaneous translation by modifying the Neural MT\ndecoder to operate with dynamically built encoder and attention. We propose a\ntunable agent which decides the best segmentation strategy for a user-defined\nBLEU loss and …

AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs

2024 · arXiv (Cornell University)

Arabic, with its rich diversity of dialects, remains significantly underrepresented in Large Language Models, particularly in dialectal variations. We address this gap by introducing seven synthetic datasets in dialects alongside Modern Standard Arabic (MSA), created …

Farasa: A Fast and Furious Segmenter for Arabic

2016

In this paper, we present Farasa, a fast and accurate Arabic segmenter. Our approach is based on SVM-rank using linear kernels. We measure the performance of the segmenter in terms of accuracy and efficiency, in …

What do Neural Machine Translation Models Learn about Morphology?

2017

Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture. However, little is known about what these models learn about source and target languages during the training process.

Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Tasks

2017 · International Joint Conference on Natural Language Processing

While neural machine translation (NMT) models provide improved translation quality in an elegant framework, it is less clear what they learn about language. Recent work has started evaluating the quality of vector representations learned by …

What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP Models

2019

Despite the remarkable evolution of deep neural networks in natural language processing (NLP), their interpretability remains a challenge. Previous work largely focused on what these models learn at the representation level. We break this analysis …