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Found 15 papers, 6 papers with code
IIE’s Neural Machine Translation Systems for WMT20
In this paper we introduce the systems IIE submitted for the WMT20 shared task on German-French news translation.
Towards Enhancing Faithfulness for Neural Machine Translation
Neural machine translation (NMT) has achieved great success due to the ability to generate high-quality sentences.
PolyLM: An Open Source Polyglot Large Language Model
Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions.
Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation
$k$-Nearest neighbor machine translation ($k$NN-MT) has attracted increasing attention due to its ability to non-parametrically adapt to new translation domains.
From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey
In this paper, we comprehensively summarize representative studies from the perspectives of dominant models, datasets and evaluation metrics.
WR-ONE2SET: Towards Well-Calibrated Keyphrase Generation
Keyphrase generation aims to automatically generate short phrases summarizing an input document.
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers
To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other.
Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation
Although data augmentation is widely used to enrich the training data, conventional methods with discrete manipulations fail to generate diverse and faithful training samples.
Know Deeper: Knowledge-Conversation Cyclic Utilization Mechanism for Open-domain Dialogue Generation
End-to-End intelligent neural dialogue systems suffer from the problems of generating inconsistent and repetitive responses.
Bi-directional CognitiveThinking Network for Machine Reading Comprehension
We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory.
Bi-directional Cognitive Thinking Network for Machine Reading Comprehension
We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory.
Uncertainty-Aware Semantic Augmentation for Neural Machine Translation
As a sequence-to-sequence generation task, neural machine translation (NMT) naturally contains intrinsic uncertainty, where a single sentence in one language has multiple valid counterparts in the other.
On Learning Universal Representations Across Languages
Recent studies have demonstrated the overwhelming advantage of cross-lingual pre-trained models (PTMs), such as multilingual BERT and XLM, on cross-lingual NLP tasks.
Contrastive Learning
Cross-Lingual Natural Language Inference
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Multiscale Collaborative Deep Models for Neural Machine Translation
Recent evidence reveals that Neural Machine Translation (NMT) models with deeper neural networks can be more effective but are difficult to train.
Unsupervised Neural Machine Translation with Future Rewarding
In this paper, we alleviate the local optimality of back-translation by learning a policy (takes the form of an encoder-decoder and is defined by its parameters) with future rewarding under the reinforcement learning framework, which aims to optimize the global word predictions for unsupervised neural machine translation.
