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Found 19 papers, 11 papers with code
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.
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.
Generative Multi-Modal Knowledge Retrieval with Large Language Models
Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-intensive multi-modal applications.
Improving Machine Translation with Large Language Models: A Preliminary Study with Cooperative Decoding
Contemporary translation engines built upon the encoder-decoder framework have reached a high level of development, while the emergence of Large Language Models (LLMs) has disrupted their position by offering the potential for achieving superior translation quality.
TIM: Teaching Large Language Models to Translate with Comparison
Open-sourced large language models (LLMs) have demonstrated remarkable efficacy in various tasks with instruction tuning.
Soft Language Clustering for Multilingual Model Pre-training
Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from source languages or when pre-training data is limited in size.
DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition
We present DualNER, a simple and effective framework to make full use of both annotated source language corpus and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER).
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.
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.
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.
Learning Confidence for Transformer-based Neural Machine Translation
Confidence estimation aims to quantify the confidence of the model prediction, providing an expectation of success.
Type-Driven Multi-Turn Corrections for Grammatical Error Correction
First, they simply mix additionally-constructed training instances and original ones to train models, which fails to help models be explicitly aware of the procedure of gradual corrections.
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings
Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models.
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.
Synonym Knowledge Enhanced Reader for Chinese Idiom Reading Comprehension
Machine reading comprehension (MRC) is the task that asks a machine to answer questions based on a given context.
Neural Simile Recognition with Cyclic Multitask Learning and Local Attention
Simile recognition is to detect simile sentences and to extract simile components, i. e., tenors and vehicles.
Graph-based Neural Sentence Ordering
Sentence ordering is to restore the original paragraph from a set of sentences.
Iterative Dual Domain Adaptation for Neural Machine Translation
Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of-domain translation knowledge to in-domain NMT model.
Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination
Based on this intuition, in this paper, we devote to distinguishing and exploiting word-level domain contexts for multi-domain NMT.
