28 citations · 80 across the 17 of their papers we have counts for
16 papers · 1 filter
X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scale
Haoran Xu, Kenton Murray, Philipp Koehn +3
Large language models (LLMs) have achieved remarkable success across various NLP tasks with a focus on English due to English-centric pre-training and limited multilingual data. In…
Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation
Haoran Xu, Amr Sharaf, Yunmo Chen +5
Moderate-sized large language models (LLMs) -- those with 7B or 13B parameters -- exhibit promising machine translation (MT) performance. However, even the top-performing 13B LLM-b…
Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles
Weiting Tan, Haoran Xu, Lingfeng Shen +5
Large language models trained primarily in a monolingual setting have demonstrated their ability to generalize to machine translation using zero- and few-shot examples with in-cont…
Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models
Tianjian Li, Haoran Xu, Philipp Koehn +2
Text generation models are notoriously vulnerable to errors in the training data. With the wide-spread availability of massive amounts of web-crawled data becoming more commonplace…
A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models
Haoran Xu, Young Jin Kim, Amr Sharaf +1
Generative Large Language Models (LLMs) have achieved remarkable advancements in various NLP tasks. However, these advances have not been reflected in the translation task, especia…
Condensing Multilingual Knowledge with Lightweight Language-Specific Modules
Haoran Xu, Weiting Tan, Shuyue Stella Li +4
Incorporating language-specific (LS) modules is a proven method to boost performance in multilingual machine translation. This approach bears similarity to Mixture-of-Experts (MoE)…
Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic Capacity
Haoran Xu, Maha Elbayad, Kenton Murray +2
Mixture-of-experts (MoE) models that employ sparse activation have demonstrated effectiveness in significantly increasing the number of parameters while maintaining low computation…
Language-Aware Multilingual Machine Translation with Self-Supervised Learning
Haoran Xu, Jean Maillard, Vedanuj Goswami
Multilingual machine translation (MMT) benefits from cross-lingual transfer but is a challenging multitask optimization problem. This is partly because there is no clear framework…
The Importance of Being Parameters: An Intra-Distillation Method for Serious Gains
Haoran Xu, Philipp Koehn, Kenton Murray
Recent model pruning methods have demonstrated the ability to remove redundant parameters without sacrificing model performance. Common methods remove redundant parameters accordin…
Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual Transfer
Haoran Xu, Kenton Murray
The current state-of-the-art for few-shot cross-lingual transfer learning first trains on abundant labeled data in the source language and then fine-tunes with a few examples on th…
VAE based Text Style Transfer with Pivot Words Enhancement Learning
Haoran Xu, Sixing Lu, Zhongkai Sun +2
Text Style Transfer (TST) aims to alter the underlying style of the source text to another specific style while keeping the same content. Due to the scarcity of high-quality parall…
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction
Mahsa Yarmohammadi, Shijie Wu, Marc Marone +10
Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other languag…
BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation
Haoran Xu, Benjamin Van Durme, Kenton Murray
The success of bidirectional encoders using masked language models, such as BERT, on numerous natural language processing tasks has prompted researchers to attempt to incorporate t…
Cross-Lingual BERT Contextual Embedding Space Mapping with Isotropic and Isometric Conditions
Haoran Xu, Philipp Koehn
Typically, a linearly orthogonal transformation mapping is learned by aligning static type-level embeddings to build a shared semantic space. In view of the analysis that contextua…
Zero-Shot Cross-Lingual Dependency Parsing through Contextual Embedding Transformation
Haoran Xu, Philipp Koehn
Linear embedding transformation has been shown to be effective for zero-shot cross-lingual transfer tasks and achieve surprisingly promising results. However, cross-lingual embeddi…
Gradual Fine-Tuning for Low-Resource Domain Adaptation
Haoran Xu, Seth Ebner, Mahsa Yarmohammadi +3
Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain. Such domain adaptatio…