Search Results for author:
Found 15 papers, 13 papers with code
Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining
In this paper, we propose the Hierarchical Argumentation Graph (Hi-ArG), a new structure to organize arguments.
Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena
Notably, even our most advanced models (GPT-4) are occasionally surpassed by heuristic baselines and human agents, highlighting the potential for further improvements in the design of LLM agents and the important role that our simulation environment can play in further testing and refining agent architectures.
Recent Advances in Direct Speech-to-text Translation
Recently, speech-to-text translation has attracted more and more attention and many studies have emerged rapidly.
Improving speech translation by fusing speech and text
To tackle these gaps, we propose \textbf{F}use-\textbf{S}peech-\textbf{T}ext (\textbf{FST}), a cross-modal model which supports three distinct input modalities for translation: speech, text, and fused speech-text.
DUB: Discrete Unit Back-translation for Speech Translation
The key point is to bridge the modality gap between speech and text so that useful MT techniques can be applied to ST.
WACO: Word-Aligned Contrastive Learning for Speech Translation
In this paper, we propose Word-Aligned COntrastive learning (WACO), a simple and effective method for extremely low-resource speech-to-text translation.
On the Impact of Noises in Crowd-Sourced Data for Speech Translation
Training speech translation (ST) models requires large and high-quality datasets.
Cross-modal Contrastive Learning for Speech Translation
Learning similar representations for semantically similar speech and text is important for speech translation.
GigaST: A 10,000-hour Pseudo Speech Translation Corpus
The training set is translated by a strong machine translation system and the test set is translated by human.
STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation
How to learn a better speech representation for end-to-end speech-to-text translation (ST) with limited labeled data?
The Volctrans Neural Speech Translation System for IWSLT 2021
For offline speech translation, our best end-to-end model achieves 8. 1 BLEU improvements over the benchmark on the MuST-C test set and is even approaching the results of a strong cascade solution.
End-to-end Speech Translation via Cross-modal Progressive Training
XSTNet takes both speech and text as input and outputs both transcription and translation text.
NeurST: Neural Speech Translation Toolkit
NeurST is an open-source toolkit for neural speech translation.
Ranked #1 on
Speech-to-Text Translation
on libri-trans
"Listen, Understand and Translate": Triple Supervision Decouples End-to-end Speech-to-text Translation
Can we build a system to fully utilize signals in a parallel ST corpus?
Variational Template Machine for Data-to-Text Generation
We propose the variational template machine (VTM), a novel method to generate text descriptions from data tables.
