Search Results for author:
Found 29 papers, 5 papers with code
Can Data Diversity Enhance Learning Generalization?
This paper introduces our Diversity Advanced Actor-Critic reinforcement learning (A2C) framework (DAAC) to improve the generalization and accuracy of Natural Language Processing (NLP).
Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out Training
Generalising to unseen domains is under-explored and remains a challenge in neural machine translation.
ITEm: Unsupervised Image-Text Embedding Learning for eCommerce
How to teach a model to learn embedding from different modalities without neglecting information from the less dominant modality is challenging.
Document-Level Language Models for Machine Translation
Finally, we explore language model fusion in the light of recent advancements in large language models.
Active Continual Learning: On Balancing Knowledge Retention and Learnability
Acquiring new knowledge without forgetting what has been learned in a sequence of tasks is the central focus of continual learning (CL).
Energy-based Self-Training and Normalization for Unsupervised Domain Adaptation
EBL can be used to improve the instance selection for a self-training task on the unlabelled target domain, and 2. alignment and normalizing energy scores can learn domain-invariant representations.
Can Domains Be Transferred Across Languages in Multi-Domain Multilingual Neural Machine Translation?
Previous works mostly focus on either multilingual or multi-domain aspects of neural machine translation (NMT).
Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic masking
We propose a multi-armed bandit framework for the sequential selection of TLM pre-training hyperparameters, aimed at optimizing language model performance, in a resource efficient manner.
A Preordered RNN Layer Boosts Neural Machine Translation in Low Resource Settings
Neural Machine Translation (NMT) models are strong enough to convey semantic and syntactic information from the source language to the target language.
Integrated Training for Sequence-to-Sequence Models Using Non-Autoregressive Transformer
Complex natural language applications such as speech translation or pivot translation traditionally rely on cascaded models.
Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer
Pivot-based neural machine translation (NMT) is commonly used in low-resource setups, especially for translation between non-English language pairs.
Back-translation for Large-Scale Multilingual Machine Translation
Surprisingly, the smaller size of vocabularies perform better, and the extensive monolingual English data offers a modest improvement.
Diving Deep into Context-Aware Neural Machine Translation
Context-aware neural machine translation (NMT) is a promising direction to improve the translation quality by making use of the additional context, e. g., document-level translation, or having meta-information.
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages
We present effective pre-training strategies for neural machine translation (NMT) using parallel corpora involving a pivot language, i. e., source-pivot and pivot-target, leading to a significant improvement in source-target translation.
Generalizing Back-Translation in Neural Machine Translation
Back-translation - data augmentation by translating target monolingual data - is a crucial component in modern neural machine translation (NMT).
Word-based Domain Adaptation for Neural Machine Translation
Pre-training a model with word weights improves fine-tuning up to 1. 24% BLEU absolute and 1. 64% TER, respectively.
Learning Bilingual Sentence Embeddings via Autoencoding and Computing Similarities with a Multilayer Perceptron
We propose a novel model architecture and training algorithm to learn bilingual sentence embeddings from a combination of parallel and monolingual data.
Learning from Chunk-based Feedback in Neural Machine Translation
We empirically investigate learning from partial feedback in neural machine translation (NMT), when partial feedback is collected by asking users to highlight a correct chunk of a translation.
Can Neural Machine Translation be Improved with User Feedback?
We present the first real-world application of methods for improving neural machine translation (NMT) with human reinforcement, based on explicit and implicit user feedback collected on the eBay e-commerce platform.
Neural and Statistical Methods for Leveraging Meta-information in Machine Translation
In this paper, we discuss different methods which use meta information and richer context that may accompany source language input to improve machine translation quality.
Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search
In this paper, we introduce a hybrid search for attention-based neural machine translation (NMT).
Graph-Based Semi-Supervised Conditional Random Fields For Spoken Language Understanding Using Unaligned Data
We experiment graph-based Semi-Supervised Learning (SSL) of Conditional Random Fields (CRF) for the application of Spoken Language Understanding (SLU) on unaligned data.
Neural Machine Translation on Scarce-Resource Condition: A case-study on Persian-English
This new loss function yields a total of 1. 87 point improvements in terms of BLEU score in the translation quality.
Guided Alignment Training for Topic-Aware Neural Machine Translation
In this paper, we propose an effective way for biasing the attention mechanism of a sequence-to-sequence neural machine translation (NMT) model towards the well-studied statistical word alignment models.
A Holistic Approach to Bilingual Sentence Fragment Extraction from Comparable Corpora
Achieving accurate translation, especially in multiple domain documents with statistical machine translation systems, requires more and more bilingual texts and this need becomes more critical when training such systems for language pairs with scarce training data.
