Search Results for author:
Found 10 papers, 2 papers with code
End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning
In this paper, we describe the system submitted to the IWSLT 2020 Offline Speech Translation Task.
Ranked #3 on
Speech-to-Text Translation
on MuST-C EN->DE
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning
In this paper, we describe end-to-end simultaneous speech-to-text and text-to-text translation systems submitted to IWSLT2020 online translation challenge.
Faster Re-translation Using Non-Autoregressive Model For Simultaneous Neural Machine Translation
The current re-translation approaches are based on autoregressive sequence generation models (ReTA), which generate tar-get tokens in the (partial) translation sequentially.
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks
Inspired by these learning patterns in humans, we suggest a simple yet generic task aware framework to incorporate into existing joint learning strategies.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
TASK AWARE MULTI-TASK LEARNING FOR SPEECH TO TEXT TASKS
In general, the direct Speech-to-text translation (ST) is jointly trained with Automatic Speech Recognition (ASR), and Machine Translation (MT) tasks.
Ranked #1 on
Speech-to-Text Translation
on MuST-C EN->DE
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Infusing Future Information into Monotonic Attention Through Language Models
Simultaneous neural machine translation(SNMT) models start emitting the target sequence before they have processed the source sequence.
Decision Attentive Regularization to Improve Simultaneous Speech Translation Systems
Hence, read/write decision policy remains the same across different input modalities, i. e., speech and text.
FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering
Using our proposed method, we outperform the current state-of-the-art method by $2. 5$ Exact Match score on the Natural Question dataset while using only $25\%$ of parameters and $35\%$ of the latency during inference, and $4. 4$ Exact Match on WebQuestions dataset.
Ranked #1 on
Question Answering
on WebQuestions
(using extra training data)
Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models
In spite of their huge success, transformer models remain difficult to scale in depth.
Language Model Augmented Monotonic Attention for Simultaneous Translation
The state-of-the-art adaptive policies for Simultaneous Neural Machine Translation (SNMT) use monotonic attention to perform read/write decisions based on the partial source and target sequences.
