565 citations · 632 across the 20 of their papers we have counts for
21 papers
FoleyGen: Visually-Guided Audio Generation
Xinhao Mei, Varun Nagaraja, Gael Le Lan +4
Recent advancements in audio generation have been spurred by the evolution of large-scale deep learning models and expansive datasets. However, the task of video-to-audio (V2A) gen…
Stack-and-Delay: a new codebook pattern for music generation
Gael Le Lan, Varun Nagaraja, Ernie Chang +5
In language modeling based music generation, a generated waveform is represented by a sequence of hierarchical token stacks that can be decoded either in an auto-regressive manner…
Enhance audio generation controllability through representation similarity regularization
Yangyang Shi, Gael Le Lan, Varun Nagaraja +6
This paper presents an innovative approach to enhance control over audio generation by emphasizing the alignment between audio and text representations during model training. In th…
Folding Attention: Memory and Power Optimization for On-Device Transformer-based Streaming Speech Recognition
Yang Li, Liangzhen Lai, Yuan Shangguan +5
Transformer-based models excel in speech recognition. Existing efforts to optimize Transformer inference, typically for long-context applications, center on simplifying attention s…
Low-Resource Cross-Lingual Adaptive Training for Nigerian Pidgin
Pin-Jie Lin, Muhammed Saeed, Ernie Chang +1
Developing effective spoken language processing systems for low-resource languages poses several challenges due to the lack of parallel data and limited resources for fine-tuning m…
Revisiting Sample Size Determination in Natural Language Understanding
Ernie Chang, Muhammad Hassan Rashid, Pin-Jie Lin +4
Knowing exactly how many data points need to be labeled to achieve a certain model performance is a hugely beneficial step towards reducing the overall budgets for annotation. It p…
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
Zechun Liu, Barlas Oguz, Changsheng Zhao +6
Several post-training quantization methods have been applied to large language models (LLMs), and have been shown to perform well down to 8-bits. We find that these methods break d…
MDIA: A Benchmark for Multilingual Dialogue Generation in 46 Languages
Qingyu Zhang, Xiaoyu Shen, Ernie Chang +2
Owing to the lack of corpora for low-resource languages, current works on dialogue generation have mainly focused on English. In this paper, we present mDIA, the first large-scale…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation
David Ifeoluwa Adelani, Jesujoba Oluwadara Alabi, Angela Fan +42
Recent advances in the pre-training of language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these dat…
The SelectGen Challenge: Finding the Best Training Samples for Few-Shot Neural Text Generation
Ernie Chang, Xiaoyu Shen, Alex Marin +1
We propose a shared task on training instance selection for few-shot neural text generation. Large-scale pretrained language models have led to dramatic improvements in few-shot te…
Time-Aware Ancient Chinese Text Translation and Inference
Ernie Chang, Yow-Ting Shiue, Hui-Syuan Yeh +1
In this paper, we aim to address the challenges surrounding the translation of ancient Chinese text: (1) The linguistic gap due to the difference in eras results in translations th…
On Training Instance Selection for Few-Shot Neural Text Generation
Ernie Chang, Xiaoyu Shen, Hui-Syuan Yeh +1
Large-scale pretrained language models have led to dramatic improvements in text generation. Impressive performance can be achieved by finetuning only on a small number of instance…
Neural Data-to-Text Generation with LM-based Text Augmentation
Ernie Chang, Xiaoyu Shen, Dawei Zhu +2
For many new application domains for data-to-text generation, the main obstacle in training neural models consists of a lack of training data. While usually large numbers of instan…
Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learning
Ernie Chang, Hui-Syuan Yeh, Vera Demberg
Recent advancements in data-to-text generation largely take on the form of neural end-to-end systems. Efforts have been dedicated to improving text generation systems by changing t…
Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling
Ernie Chang, Vera Demberg, Alex Marin
Neural natural language generation (NLG) and understanding (NLU) models are data-hungry and require massive amounts of annotated data to be competitive. Recent frameworks address t…
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool
Ernie Chang, Jeriah Caplinger, Alex Marin +2
We present a lightweight annotation tool, the Data AnnotatoR Tool (DART), for the general task of labeling structured data with textual descriptions. The tool is implemented as an…
Safe Handover in Mixed-Initiative Control for Cyber-Physical Systems
Frederik Wiehr, Anke Hirsch, Florian Daiber +7
For mixed-initiative control between cyber-physical systems (CPS) and its users, it is still an open question how machines can safely hand over control to humans. In this work, we…
Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence
Xiaoyu Shen, Ernie Chang, Hui Su +2
The neural attention model has achieved great success in data-to-text generation tasks. Though usually excelling at producing fluent text, it suffers from the problem of informatio…
Unsupervised Pidgin Text Generation By Pivoting English Data and Self-Training
Ernie Chang, David Ifeoluwa Adelani, Xiaoyu Shen +1
West African Pidgin English is a language that is significantly spoken in West Africa, consisting of at least 75 million speakers. Nevertheless, proper machine translation systems…
Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model
Wenli Zhuang, Ernie Chang
This paper describes a neural-network model which performed competitively (top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS) task. Our system employs an at…