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Found 14 papers, 4 papers with code
Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization
Specifically, we first conduct domain-aware pre-training using large-scale multi-scenario multi-domain dialogue data to enhance the adaptability of our pre-trained model.
Life-long Learning for Multilingual Neural Machine Translation with Knowledge Distillation
To alleviate the CF, we investigate knowledge distillation based life-long learning methods.
Neural Machine Translation with Explicit Phrase Alignment
The lack of alignment in NMT models leads to three problems: it is hard to (1) interpret the translation process, (2) impose lexical constraints, and (3) impose structural constraints.
A Compact and Language-Sensitive Multilingual Translation Method
We verify our methods on various translation scenarios, including one-to-many, many-to-many and zero-shot.
Improving the Transformer Translation Model with Document-Level Context
Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-art performance in a variety of translation tasks, how to use document-level context to deal with discourse phenomena problematic for Transformer still remains a challenge.
Three Strategies to Improve One-to-Many Multilingual Translation
However, previous studies show that one-to-many translation based on this framework cannot perform on par with the individually trained models.
Neural Models for Sequence Chunking
Many natural language understanding (NLU) tasks, such as shallow parsing (i. e., text chunking) and semantic slot filling, require the assignment of representative labels to the meaningful chunks in a sentence.
SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art.
Ranked #8 on
Text Summarization
on CNN / Daily Mail (Anonymized)
Unsupervised Tree Induction for Tree-based Translation
In current research, most tree-based translation models are built directly from parse trees.
