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09/08/2023
Linking Symptom Inventories using Semantic Textual Similarity
An extensive library of symptom inventories has been developed over time...
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07/13/2023
MegaWika: Millions of reports and their sources across 50 diverse languages
To foster the development of new models for collaborative AI-assisted re...
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05/27/2023
Why Does Zero-Shot Cross-Lingual Generation Fail? An Explanation and a Solution
Zero-shot cross-lingual transfer is when a multilingual model is trained...
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05/23/2023
Exploring Representational Disparities Between Multilingual and Bilingual Translation Models
Multilingual machine translation has proven immensely useful for low-res...
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05/23/2023
Condensing Multilingual Knowledge with Lightweight Language-Specific Modules
Incorporating language-specific (LS) modules is a proven method to boost...
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05/03/2023
Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic Capacity
Mixture-of-experts (MoE) models that employ sparse activation have demon...
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11/14/2022
Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns
In this work, we focus on intrasentential code-mixing and propose severa...
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05/23/2022
The Importance of Being Parameters: An Intra-Distillation Method for Serious Gains
Recent model pruning methods have demonstrated the ability to remove red...
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04/29/2022
Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual Transfer
The current state-of-the-art for few-shot cross-lingual transfer learnin...
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01/20/2022
Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models
The advent of transformer-based models such as BERT has led to the rise ...
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09/14/2021
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction
Zero-shot cross-lingual information extraction (IE) describes the constr...
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09/09/2021
BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation
The success of bidirectional encoders using masked language models, such...
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05/04/2021
Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution
In this paper, we investigate the driving factors behind concatenation, ...
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04/12/2021
Joint Universal Syntactic and Semantic Parsing
While numerous attempts have been made to jointly parse syntax and seman...
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03/03/2021
Gradual Fine-Tuning for Low-Resource Domain Adaptation
Fine-tuning is known to improve NLP models by adapting an initial model ...
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10/16/2019
Efficiency through Auto-Sizing: Notre Dame NLP's Submission to the WNGT 2019 Efficiency Task
This paper describes the Notre Dame Natural Language Processing Group's ...
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10/01/2019
Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation
Neural sequence-to-sequence models, particularly the Transformer, are th...
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11/02/2018
An Empirical Exploration of Curriculum Learning for Neural Machine Translation
Machine translation systems based on deep neural networks are expensive ...
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08/29/2018
Correcting Length Bias in Neural Machine Translation
We study two problems in neural machine translation (NMT). First, in bea...
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02/13/2016
Semantic Scan: Detecting Subtle, Spatially Localized Events in Text Streams
Early detection and precise characterization of emerging topics in text ...
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08/20/2015
