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Found 48 papers, 24 papers with code
Edinburgh’s End-to-End Multilingual Speech Translation System for IWSLT 2021
This paper describes Edinburgh’s submissions to the IWSLT2021 multilingual speech translation (ST) task.
ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT’20 Metrics Shared Task
We describe parBLEU, parCHRF++, and parESIM, which augment baseline metrics with automatically generated paraphrases produced by PRISM (Thompson and Post, 2020a), a multilingual neural machine translation system.
MADLAD-400: A Multilingual And Document-Level Large Audited Dataset
We introduce MADLAD-400, a manually audited, general domain 3T token monolingual dataset based on CommonCrawl, spanning 419 languages.
InstructEdit: Improving Automatic Masks for Diffusion-based Image Editing With User Instructions
In this work, we propose a framework termed InstructEdit that can do fine-grained editing based on user instructions.
When Does Monolingual Data Help Multilingual Translation: The Role of Domain and Model Scale
Next, we analyze the impact of scale (from 90M to 1. 6B parameters) and find it is important for both methods, particularly DAE.
SLTUNET: A Simple Unified Model for Sign Language Translation
We propose SLTUNET, a simple unified neural model designed to support multiple SLTrelated tasks jointly, such as sign-to-gloss, gloss-to-text and sign-to-text translation.
MDP: A Generalized Framework for Text-Guided Image Editing by Manipulating the Diffusion Path
Image generation using diffusion can be controlled in multiple ways.
Efficient CTC Regularization via Coarse Labels for End-to-End Speech Translation
For end-to-end speech translation, regularizing the encoder with the Connectionist Temporal Classification (CTC) objective using the source transcript or target translation as labels can greatly improve quality metrics.
3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models
We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for generative diffusion models.
Prompting Large Language Model for Machine Translation: A Case Study
Research on prompting has shown excellent performance with little or even no supervised training across many tasks.
Investigating Neuron Disturbing in Fusing Heterogeneous Neural Networks
Fusing deep learning models trained on separately located clients into a global model in a one-shot communication round is a straightforward implementation of Federated Learning.
Monolith: Real Time Recommendation System With Collisionless Embedding Table
In this paper, we present Monolith, a system tailored for online training.
Revisiting End-to-End Speech-to-Text Translation From Scratch
Finally, we discuss neural acoustic feature modeling, where a neural model is designed to extract acoustic features from raw speech signals directly, with the goal to simplify inductive biases and add freedom to the model in describing speech.
3DILG: Irregular Latent Grids for 3D Generative Modeling
All probabilistic experiments confirm that we are able to generate detailed and high quality shapes to yield the new state of the art in generative 3D shape modeling.
Data Scaling Laws in NMT: The Effect of Noise and Architecture
In this work, we study the effect of varying the architecture and training data quality on the data scaling properties of Neural Machine Translation (NMT).
Examining Scaling and Transfer of Language Model Architectures for Machine Translation
Natural language understanding and generation models follow one of the two dominant architectural paradigms: language models (LMs) that process concatenated sequences in a single stack of layers, and encoder-decoder models (EncDec) that utilize separate layer stacks for input and output processing.
Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents
Using simple concatenation-based DocNMT, we explore the effect of 3 factors on the transfer: the number of teacher languages with document level data, the balance between document and sentence level data at training, and the data condition of parallel documents (genuine vs. backtranslated).
Beyond Sentence-Level End-to-End Speech Translation: Context Helps
Document-level contextual information has shown benefits to text-based machine translation, but whether and how context helps end-to-end (E2E) speech translation (ST) is still under-studied.
Exploring Dynamic Selection of Branch Expansion Orders for Code Generation
Due to the great potential in facilitating software development, code generation has attracted increasing attention recently.
Sparse Attention with Linear Units
Recently, it has been argued that encoder-decoder models can be made more interpretable by replacing the softmax function in the attention with its sparse variants.
Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation
Our study further verifies the trade-off between the shared capacity and LS capacity for multilingual translation.
Fast Interleaved Bidirectional Sequence Generation
Instead of assuming independence between neighbouring tokens (semi-autoregressive decoding, SA), we take inspiration from bidirectional sequence generation and introduce a decoder that generates target words from the left-to-right and right-to-left directions simultaneously.
Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization
We combine training data generating networks with bi-level optimization algorithms to obtain a complete framework for which all components can be jointly trained.
Adaptive Feature Selection for End-to-End Speech Translation
Information in speech signals is not evenly distributed, making it an additional challenge for end-to-end (E2E) speech translation (ST) to learn to focus on informative features.
A Study in Improving BLEU Reference Coverage with Diverse Automatic Paraphrasing
We investigate a long-perceived shortcoming in the typical use of BLEU: its reliance on a single reference.
Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation
Massively multilingual models for neural machine translation (NMT) are theoretically attractive, but often underperform bilingual models and deliver poor zero-shot translations.
On Sparsifying Encoder Outputs in Sequence-to-Sequence Models
Inspired by these observations, we explore the feasibility of specifying rule-based patterns that mask out encoder outputs based on information such as part-of-speech tags, word frequency and word position.
Point Cloud Instance Segmentation using Probabilistic Embeddings
In this paper we propose a new framework for point cloud instance segmentation.
Ranked #1 on
Instance Segmentation
on PartNet
Root Mean Square Layer Normalization
RMSNorm regularizes the summed inputs to a neuron in one layer according to root mean square (RMS), giving the model re-scaling invariance property and implicit learning rate adaptation ability.
Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention
The general trend in NLP is towards increasing model capacity and performance via deeper neural networks.
A Lightweight Recurrent Network for Sequence Modeling
We apply LRN as a drop-in replacement of existing recurrent units in several neural sequential models.
Revisiting Low-Resource Neural Machine Translation: A Case Study
It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions, underperforming phrase-based statistical machine translation (PBSMT) and requiring large amounts of auxiliary data to achieve competitive results.
Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks
Experiments on WMT14 translation tasks demonstrate that ATR-based neural machine translation can yield competitive performance on English- German and English-French language pairs in terms of both translation quality and speed.
Otem&Utem: Over- and Under-Translation Evaluation Metric for NMT
Although neural machine translation(NMT) yields promising translation performance, it unfortunately suffers from over- and under-translation is- sues [Tu et al., 2016], of which studies have become research hotspots in NMT.
Accelerating Neural Transformer via an Average Attention Network
To alleviate this issue, we propose an average attention network as an alternative to the self-attention network in the decoder of the neural Transformer.
Ranked #59 on
Machine Translation
on WMT2014 English-German
Attention Regularized Sequence-to-Sequence Learning for E2E NLG Challenge
This paper describes our system used for the end-to-end (E2E) natural language generation (NLG) challenge.
Ranked #7 on
Data-to-Text Generation
on E2E NLG Challenge
Variational Recurrent Neural Machine Translation
Partially inspired by successful applications of variational recurrent neural networks, we propose a novel variational recurrent neural machine translation (VRNMT) model in this paper.
A GRU-Gated Attention Model for Neural Machine Translation
In this paper, we propose a novel GRU-gated attention model (GAtt) for NMT which enhances the degree of discrimination of context vectors by enabling source representations to be sensitive to the partial translation generated by the decoder.
Convolution-Enhanced Bilingual Recursive Neural Network for Bilingual Semantic Modeling
After that, we fully incorporate information of different linguistic units into a bilinear semantic similarity model.
Bilingual Autoencoders with Global Descriptors for Modeling Parallel Sentences
Parallel sentence representations are important for bilingual and cross-lingual tasks in natural language processing.
Cseq2seq: Cyclic Sequence-to-Sequence Learning
The vanilla sequence-to-sequence learning (seq2seq) reads and encodes a source sequence into a fixed-length vector only once, suffering from its insufficiency in modeling structural correspondence between the source and target sequence.
BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings
In this paper, we propose a bidimensional attention based recursive autoencoder (BattRAE) to integrate clues and sourcetarget interactions at multiple levels of granularity into bilingual phrase representations.
Variational Neural Machine Translation
Models of neural machine translation are often from a discriminative family of encoderdecoders that learn a conditional distribution of a target sentence given a source sentence.
Neural Discourse Relation Recognition with Semantic Memory
Inspired by this, we propose a neural recognizer for implicit discourse relation analysis, which builds upon a semantic memory that stores knowledge in a distributed fashion.
Variational Neural Discourse Relation Recognizer
In order to perform efficient inference and learning, we introduce neural discourse relation models to approximate the prior and posterior distributions of the latent variable, and employ these approximated distributions to optimize a reparameterized variational lower bound.
Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising
As compared to the conventional RGB or gray-scale images, multispectral images (MSI) can deliver more faithful representation for real scenes, and enhance the performance of many computer vision tasks.
