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Found 77 papers, 19 papers with code
STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework
Simultaneous translation, which translates sentences before they are finished, is useful in many scenarios but is notoriously difficult due to word-order differences.
PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit
PaddleSpeech is an open-source all-in-one speech toolkit.
Automatic Speech Recognition (ASR)
Environmental Sound Classification
+9
ERNIE-SAT: Speech and Text Joint Pretraining for Cross-Lingual Multi-Speaker Text-to-Speech
In this paper, we extend the pretraining method for cross-lingual multi-speaker speech synthesis tasks, including cross-lingual multi-speaker voice cloning and cross-lingual multi-speaker speech editing.
A$^3$T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing
Recently, speech representation learning has improved many speech-related tasks such as speech recognition, speech classification, and speech-to-text translation.
Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction
The hierarchical attentive aggregation can capture spatial dependencies among atoms, as well as fuse the position-enhanced information with the capability of discriminating multiple spatial relations among atoms.
Deep Reinforcement Learning for Online Offloading in Wireless Powered Mobile-Edge Computing Networks
To tackle this problem, we propose in this paper a Deep Reinforcement learning-based Online Offloading (DROO) framework that implements a deep neural network to generate offloading decisions.
Networking and Internet Architecture
Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks
In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability and average power constraints.
Edge-computing
Networking and Internet Architecture
Algorithm for Optimized mRNA Design Improves Stability and Immunogenicity
Messenger RNA (mRNA) vaccines are being used for COVID-19, but still suffer from the critical issue of mRNA instability and degradation, which is a major obstacle in the storage, distribution, and efficacy of the vaccine.
Dependency-based Convolutional Neural Networks for Sentence Embedding
In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results, but all such efforts process word vectors sequentially and neglect long-distance dependencies.
Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles
Parsing accuracy using efficient greedy transition systems has improved dramatically in recent years thanks to neural networks.
Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity
To this end, we propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool).
Ranked #3 on
Protein-Ligand Affinity Prediction
on PDBbind
Context-aware Stand-alone Neural Spelling Correction
Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings.
Textual Entailment with Structured Attentions and Composition
We show that it is beneficial to extend the attention model to tree nodes between premise and hypothesis.
Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set
We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features.
Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank
Discourse parsing has long been treated as a stand-alone problem independent from constituency or dependency parsing.
Undesignable RNA Structure Identification via Rival Structure Generation and Structure Decomposition
RNA design is the search for a sequence or set of sequences that will fold into predefined structures, also known as the inverse problem of RNA folding.
LinearAlifold: Linear-Time Consensus Structure Prediction for RNA Alignments
We present LinearAlifold, an efficient algorithm for folding aligned RNA homologs that scales linearly with both the sequence length and the number of sequences, based on our recent work LinearFold that folds a single RNA in linear time.
Messenger RNA Design via Expected Partition Function and Continuous Optimization
We then use gradient descent-based optimization methods to improve the extended objective function, and the distribution will gradually shrink towards a one-hot sequence (i. e., a single sequence).
Linear-Time Constituency Parsing with RNNs and Dynamic Programming
However, the minimal span parser of Stern et al (2017a) which holds the current state of the art accuracy is a chart parser running in cubic time, $O(n^3)$, which is too slow for longer sentences and for applications beyond sentence boundaries such as end-to-end discourse parsing and joint sentence boundary detection and parsing.
Event Nugget Detection with Forward-Backward Recurrent Neural Networks
Traditional event detection methods heavily rely on manually engineered rich features.
OSU Multimodal Machine Translation System Report
This paper describes Oregon State University's submissions to the shared WMT'17 task "multimodal translation task I".
Group Sparse CNNs for Question Classification with Answer Sets
Question classification is an important task with wide applications.
Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks
In order to utilize the potential benefits from their correlations, we propose a jointly trained model for learning the two tasks simultaneously via Long Short-Term Memory (LSTM) networks.
Incremental Parsing with Minimal Features Using Bi-Directional LSTM
Recently, neural network approaches for parsing have largely automated the combination of individual features, but still rely on (often a larger number of) atomic features created from human linguistic intuition, and potentially omitting important global context.
Type-Driven Incremental Semantic Parsing with Polymorphism
Semantic parsing has made significant progress, but most current semantic parsers are extremely slow (CKY-based) and rather primitive in representation.
Ranked #4 on
Semantic Parsing
on ATIS
Large Margin Neural Language Model
We propose a large margin criterion for training neural language models.
Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation
Beam search is widely used in neural machine translation, and usually improves translation quality compared to greedy search.
Multi-Reference Training with Pseudo-References for Neural Translation and Text Generation
Neural text generation, including neural machine translation, image captioning, and summarization, has been quite successful recently.
Ensemble Sequence Level Training for Multimodal MT: OSU-Baidu WMT18 Multimodal Machine Translation System Report
This paper describes multimodal machine translation systems developed jointly by Oregon State University and Baidu Research for WMT 2018 Shared Task on multimodal translation.
When to Finish? Optimal Beam Search for Neural Text Generation (modulo beam size)
In neural text generation such as neural machine translation, summarization, and image captioning, beam search is widely used to improve the output text quality.
Speeding Up Neural Machine Translation Decoding by Cube Pruning
Although neural machine translation has achieved promising results, it suffers from slow translation speed.
Robust Neural Machine Translation with Joint Textual and Phonetic Embedding
Neural machine translation (NMT) is notoriously sensitive to noises, but noises are almost inevitable in practice.
Learning to Stop in Structured Prediction for Neural Machine Translation
Beam search optimization resolves many issues in neural machine translation.
Simultaneous Translation with Flexible Policy via Restricted Imitation Learning
Simultaneous translation is widely useful but remains one of the most difficult tasks in NLP.
Robust Machine Translation with Domain Sensitive Pseudo-Sources: Baidu-OSU WMT19 MT Robustness Shared Task System Report
To make it worse, the amount of social media parallel corpora is extremely limited.
Speculative Beam Search for Simultaneous Translation
Beam search is universally used in full-sentence translation but its application to simultaneous translation remains non-trivial, where output words are committed on the fly.
Machine Translation in Pronunciation Space
The research in machine translation community focus on translation in text space.
Incremental Text-to-Speech Synthesis with Prefix-to-Prefix Framework
Text-to-speech synthesis (TTS) has witnessed rapid progress in recent years, where neural methods became capable of producing audios with high naturalness.
Data Augmentation for Deep Learning-based Radio Modulation Classification
Deep learning has recently been applied to automatically classify the modulation categories of received radio signals without manual experience.
Simultaneous Translation Policies: From Fixed to Adaptive
Adaptive policies are better than fixed policies for simultaneous translation, since they can flexibly balance the tradeoff between translation quality and latency based on the current context information.
Opportunistic Decoding with Timely Correction for Simultaneous Translation
Simultaneous translation has many important application scenarios and attracts much attention from both academia and industry recently.
Visualizing Deep Learning-based Radio Modulation Classifier
Extensive numerical results show both the CNN-based classifier and LSTM-based classifier extract similar radio features relating to modulation reference points.
NEJM-enzh: A Parallel Corpus for English-Chinese Translation in the Biomedical Domain
We show that training on out-of-domain data and fine-tuning with as few as 4, 000 NEJM sentence pairs improve translation quality by 25. 3 (13. 4) BLEU for en$\to$zh (zh$\to$en) directions.
Fluent and Low-latency Simultaneous Speech-to-Speech Translation with Self-adaptive Training
Simultaneous speech-to-speech translation is widely useful but extremely challenging, since it needs to generate target-language speech concurrently with the source-language speech, with only a few seconds delay.
MAM: Masked Acoustic Modeling for End-to-End Speech-to-Text Translation
End-to-end Speech-to-text Translation (E2E-ST), which directly translates source language speech to target language text, is widely useful in practice, but traditional cascaded approaches (ASR+MT) often suffer from error propagation in the pipeline.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Improving Simultaneous Translation by Incorporating Pseudo-References with Fewer Reorderings
Simultaneous translation is vastly different from full-sentence translation, in the sense that it starts translation before the source sentence ends, with only a few words delay.
SigNet: A Novel Deep Learning Framework for Radio Signal Classification
More interestingly, our proposed models behave extremely well in small-sample learning when only a small training dataset is provided.
Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation
Recently, representation learning for text and speech has successfully improved many language related tasks.
[Re] Explaining Groups of Points in Low-Dimensional Representations
Furthermore, we have created our own implementation of the algorithm in which we have incorporated additional experiments in order to evaluate the algorithmʼs relevance in the scope of different dimensionality reduction techniques and differently structured data.
Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized Streaming ASR
Simultaneous speech-to-text translation is widely useful in many scenarios.
Simultaneous Translation
Simultaneous translation, which performs translation concurrently with the source speech, is widely useful in many scenarios such as international conferences, negotiations, press releases, legal proceedings, and medicine.
The Role of Phonetic Units in Speech Emotion Recognition
Models of phonemes, broad phonetic classes, and syllables all significantly outperform the utterance model, demonstrating that phonetic units are helpful and should be incorporated in speech emotion recognition.
Decoupling recognition and transcription in Mandarin ASR
Much of the recent literature on automatic speech recognition (ASR) is taking an end-to-end approach.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Computation Rate Maximum for Mobile Terminals in UAV-assisted Wireless Powered MEC Networks with Fairness Constraint
This paper investigates an unmanned aerial vehicle (UAV)-assisted wireless powered mobile-edge computing (MEC) system, where the UAV powers the mobile terminals by wireless power transfer (WPT) and provides computation service for them.
Prediction and Control of Focal Seizure Spread: Random Walk with Restart on Heterogeneous Brain Networks
In this study, we used a whole-brain model to show that heterogeneity in nodal excitability had a significant impact on seizure propagation in the networks, and compromised the prediction accuracy with structural connections.
Data-Driven Adaptive Simultaneous Machine Translation
In simultaneous translation (SimulMT), the most widely used strategy is the wait-k policy thanks to its simplicity and effectiveness in balancing translation quality and latency.
A Fast Attention Network for Joint Intent Detection and Slot Filling on Edge Devices
In this paper, we propose a Fast Attention Network (FAN) for joint intent detection and slot filling tasks, guaranteeing both accuracy and latency.
Findings of the Third Workshop on Automatic Simultaneous Translation
This paper reports the results of the shared task we hosted on the Third Workshop of Automatic Simultaneous Translation (AutoSimTrans).
LinearCoFold and LinearCoPartition: Linear-Time Algorithms for Secondary Structure Prediction of Interacting RNA molecules
Vienna RNAcofold, which reduces the problem into the classical single sequence folding by concatenating two strands, scales in cubic time against the combined sequence length, and is slow for long sequences.
LinearSankoff: Linear-time Simultaneous Folding and Alignment of RNA Homologs
First, it takes $O(n^6)$ for two sequences where n is the average sequence length.
