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Found 21 papers, 15 papers with code
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.
Prompt-Driven Neural Machine Translation
Neural machine translation (NMT) has obtained significant performance improvement over the recent years.
Semformer: Transformer Language Models with Semantic Planning
In this paper, we introduce Semformer, a novel method of training a Transformer language model that explicitly models the semantic planning of response.
LexMatcher: Dictionary-centric Data Collection for LLM-based Machine Translation
The fine-tuning of open-source large language models (LLMs) for machine translation has recently received considerable attention, marking a shift towards data-centric research from traditional neural machine translation.
What Have We Achieved on Non-autoregressive Translation?
Recent advances have made non-autoregressive (NAT) translation comparable to autoregressive methods (AT).
ShennongAlpha: an AI-driven sharing and collaboration platform for intelligent curation, acquisition, and translation of natural medicinal material knowledge
Natural Medicinal Materials (NMMs) have a long history of global clinical applications and a wealth of records and knowledge.
Improving Machine Translation with Large Language Models: A Preliminary Study with Cooperative Decoding
Contemporary translation engines based on the encoder-decoder framework have made significant strides in development.
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.
Explicit Syntactic Guidance for Neural Text Generation
Most existing text generation models follow the sequence-to-sequence paradigm.
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.
Multi-Granularity Optimization for Non-Autoregressive Translation
Despite low latency, non-autoregressive machine translation (NAT) suffers severe performance deterioration due to the naive independence assumption.
Categorizing Semantic Representations for Neural Machine Translation
Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks.
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.
On Compositional Generalization of Neural Machine Translation
Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks such as WMT.
Dynamic Context-guided Capsule Network for Multimodal Machine Translation
Particularly, we represent the input image with global and regional visual features, we introduce two parallel DCCNs to model multimodal context vectors with visual features at different granularities.
Ranked #3 on
Multimodal Machine Translation
on Multi30K
A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation
Multi-modal neural machine translation (NMT) aims to translate source sentences into a target language paired with images.
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.
