Search Results for author:
Found 24 papers, 8 papers with code
ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation
Joint speech-language training is challenging due to the large demand for training data and GPU consumption, as well as the modality gap between speech and language.
i-Code Studio: A Configurable and Composable Framework for Integrative AI
Artificial General Intelligence (AGI) requires comprehensive understanding and generation capabilities for a variety of tasks spanning different modalities and functionalities.
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data
The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encoder-only models which lack generative abilities.
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization
Z-Code++ creates new state of the art on 9 out of 13 text summarization tasks across 5 languages.
i-Code: An Integrative and Composable Multimodal Learning Framework
Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview.
Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention
In particular, we focus on the task of Commonsense Reasoning, demonstrating that the proposed external attention mechanism can augment existing transformer models and significantly improve the model's reasoning capabilities.
Ranked #2 on
Common Sense Reasoning
on CommonsenseQA
(using extra training data)
Florence: A New Foundation Model for Computer Vision
Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications.
Ranked #1 on
Action Recognition In Videos
on Kinetics-600
One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement
Experimental results show that the proposed geometry agnostic model outperforms the model trained on a specific microphone array geometry in both speech quality and automatic speech recognition accuracy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Personalized Speech Enhancement: New Models and Comprehensive Evaluation
Our results show that the proposed models can yield better speech recognition accuracy, speech intelligibility, and perceptual quality than the baseline models, and the multi-task training can alleviate the TSOS issue in addition to improving the speech recognition accuracy.
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive self-supervised learning are conducted in a multi-task learning manner.
Fusing Context Into Knowledge Graph for Commonsense Question Answering
However, although a KG contains rich structural information, it lacks the context to provide a more precise understanding of the concepts.
Ranked #3 on
Common Sense Reasoning
on CommonsenseQA
(using extra training data)
Mixed-Lingual Pre-training for Cross-lingual Summarization
Cross-lingual Summarization (CLS) aims at producing a summary in the target language for an article in the source language.
Mind The Facts: Knowledge-Boosted Coherent Abstractive Text Summarization
In this work, we propose a novel architecture that extends Transformer encoder-decoder architecture in order to improve on these shortcomings.
A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining
With the abundance of automatic meeting transcripts, meeting summarization is of great interest to both participants and other parties.
Enhancing Factual Consistency of Abstractive Summarization
Automatic abstractive summaries are found to often distort or fabricate facts in the article.
TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising
Text summarization aims to extract essential information from a piece of text and transform the text into a concise version.
Leveraging Lead Bias for Zero-shot Abstractive News Summarization
A typical journalistic convention in news articles is to deliver the most salient information in the beginning, also known as the lead bias.
Advances in Online Audio-Visual Meeting Transcription
This increases marginally to 1. 6% when 50% of the attendees are unknown to the system.
Multi-task Learning for Natural Language Generation in Task-Oriented Dialogue
In this paper, we propose a novel multi-task learning framework, NLG-LM, for natural language generation.
SIM: A Slot-Independent Neural Model for Dialogue State Tracking
In this paper, we put forward a slot-independent neural model (SIM) to track dialogue states while keeping the model complexity invariant to the number of dialogue slots.
Make Lead Bias in Your Favor: A Simple and Effective Method for News Summarization
For example, the pretrained model without finetuning outperforms pointer-generator network on CNN/DailyMail dataset.
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
Conversational question answering (CQA) is a novel QA task that requires understanding of dialogue context.
Ranked #3 on
Question Answering
on CoQA
(Overall metric)
Achieving Human Parity on Automatic Chinese to English News Translation
Machine translation has made rapid advances in recent years.
Ranked #3 on
Machine Translation
on WMT 2017 English-Chinese
