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Found 34 papers, 13 papers with code
Sequence-to-Dependency Neural Machine Translation
Nowadays a typical Neural Machine Translation (NMT) model generates translations from left to right as a linear sequence, during which latent syntactic structures of the target sentences are not explicitly concerned.
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
Regularizing Neural Machine Translation by Target-bidirectional Agreement
Although Neural Machine Translation (NMT) has achieved remarkable progress in the past several years, most NMT systems still suffer from a fundamental shortcoming as in other sequence generation tasks: errors made early in generation process are fed as inputs to the model and can be quickly amplified, harming subsequent sequence generation.
Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy
In order to avoid such sophisticated alternate optimization, we propose to learn unsupervised word mapping by directly maximizing the mean discrepancy between the distribution of transferred embedding and target embedding.
Accelerating Transformer Decoding via a Hybrid of Self-attention and Recurrent Neural Network
Due to the highly parallelizable architecture, Transformer is faster to train than RNN-based models and popularly used in machine translation tasks.
Source Dependency-Aware Transformer with Supervised Self-Attention
Recently, Transformer has achieved the state-of-the-art performance on many machine translation tasks.
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning
In addition, by transferring knowledge from other kinds of MRC tasks, our model achieves a new state-of-the-art results in both single and ensemble settings.
Ranked #1 on
Reading Comprehension
on RACE
Emotion Classification by Jointly Learning to Lexiconize and Classify
Emotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018).
Robust Machine Reading Comprehension by Learning Soft labels
Neural models have achieved great success on the task of machine reading comprehension (MRC), which are typically trained on hard labels.
Attention Calibration for Transformer in Neural Machine Translation
Attention mechanisms have achieved substantial improvements in neural machine translation by dynamically selecting relevant inputs for different predictions.
Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context
In terms of the local view, we first build a graph structure based on the document where phrases are regarded as vertices and the edges are similarities between vertices.
Pretraining without Wordpieces: Learning Over a Vocabulary of Millions of Words
(2) how to predict a word via cloze test without knowing the number of wordpieces in advance?
SkillNet-NLU: A Sparsely Activated Model for General-Purpose Natural Language Understanding
The key feature of our approach is that it is sparsely activated guided by predefined skills.
Learning Confidence for Transformer-based Neural Machine Translation
Confidence estimation aims to quantify the confidence of the model prediction, providing an expectation of success.
Task-guided Disentangled Tuning for Pretrained Language Models
Pretrained language models (PLMs) trained on large-scale unlabeled corpus are typically fine-tuned on task-specific downstream datasets, which have produced state-of-the-art results on various NLP tasks.
Modeling Multi-Granularity Hierarchical Features for Relation Extraction
In this paper, we propose a novel method to extract multi-granularity features based solely on the original input sentences.
UM4: Unified Multilingual Multiple Teacher-Student Model for Zero-Resource Neural Machine Translation
Most translation tasks among languages belong to the zero-resource translation problem where parallel corpora are unavailable.
An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework based on Semantic Blocks
To tackle this problem, in this paper, we proposed an efficient Coarse-to-Fine Facet-Aware Ranking (C2F-FAR) framework for unsupervised long document summarization, which is based on the semantic block.
Modeling Paragraph-Level Vision-Language Semantic Alignment for Multi-Modal Summarization
The core of ViL-Sum is a joint multi-modal encoder with two well-designed tasks, image reordering and image selection.
Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding
Specifically, with the help of prompts, we construct virtual semantic prototypes to each instance, and derive negative prototypes by using the negative form of the prompts.
Swing Distillation: A Privacy-Preserving Knowledge Distillation Framework
Knowledge distillation (KD) has been widely used for model compression and knowledge transfer.
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks
It broadcasts the global model in the server to each client and produces pseudo data for clients so that knowledge from the global model can be explored to enhance few-shot learning of each client model.
Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality
The global one aims to identify vital sub-topics in the dialogue and the local one aims to select the most important context in each sub-topic.
Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models
We conduct extensive experiments on various Chinese NLP tasks to evaluate existing PLMs as well as the proposed MigBERT.
Retrieval-Augmented Classification with Decoupled Representation
Retrieval augmented methods have shown promising results in various classification tasks.
Enhancing Large Language Model with Self-Controlled Memory Framework
Large Language Models (LLMs) are constrained by their inability to process lengthy inputs, resulting in the loss of critical historical information.
SkillNet-X: A Multilingual Multitask Model with Sparsely Activated Skills
Traditional multitask learning methods basically can only exploit common knowledge in task- or language-wise, which lose either cross-language or cross-task knowledge.
DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models
The ability of data memorization and regurgitation in pretrained language models, revealed in previous studies, brings the risk of data leakage.
Tencent Translation System for the WMT21 News Translation Task
Based on our success in the last WMT, we continuously employed advanced techniques such as large batch training, data selection and data filtering.
Tencent Neural Machine Translation Systems for the WMT20 News Translation Task
This paper describes Tencent Neural Machine Translation systems for the WMT 2020 news translation tasks.
Recurrent Attention for Neural Machine Translation
Across an extensive set of experiments on 10 machine translation tasks, we find that RAN models are competitive and outperform their Transformer counterpart in certain scenarios, with fewer parameters and inference time.
