Hassan Sajjad

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 …

DAFE: LLM-Based Evaluation Through Dynamic Arbitration for Free-Form Question-Answering

2025 · Qeios

Evaluating Large Language Models (LLMs) free-form generated responses remains a challenge due to their diverse and open-ended nature. Traditional supervised signal-based automatic metrics fail to capture semantic equivalence or handle the variability of open-ended responses, …

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 …