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Found 15 papers, 6 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.
Code Comparison Tuning for Code Large Language Models
We present Code Comparison Tuning (CCT), a simple and effective tuning method for code large language models (Code LLMs) to better handle subtle code errors.
RecycleGPT: An Autoregressive Language Model with Recyclable Module
Existing large language models have to run K times to generate a sequence of K tokens.
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.
DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog
Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or continuous integration scenarios.
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.
Dynamic Curriculum Learning for Low-Resource Neural Machine Translation
This eases training by highlighting easy samples that the current model has enough competence to learn.
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
Shallow-to-Deep Training for Neural Machine Translation
We find that stacking layers is helpful in improving the representation ability of NMT models and adjacent layers perform similarly.
Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation
In encoder-decoder neural models, multiple encoders are in general used to represent the contextual information in addition to the individual sentence.
Learning Architectures from an Extended Search Space for Language Modeling
Neural architecture search (NAS) has advanced significantly in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell.
Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition
In this paper, we study differentiable neural architecture search (NAS) methods for natural language processing.
Ranked #1 on
Language Modelling
on PTB Diagnostic ECG Database
