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Found 14 papers, 11 papers with code
Transfer Learning for Sequence Generation: from Single-source to Multi-source
Although directly finetuning pretrained models on MSG tasks and concatenating multiple sources into a single long sequence is regarded as a simple method to transfer pretrained models to MSG tasks, we conjecture that the direct finetuning method leads to catastrophic forgetting and solely relying on pretrained self-attention layers to capture cross-source information is not sufficient.
ComQA:Compositional Question Answering via Hierarchical Graph Neural Networks
In compositional question answering, the systems should assemble several supporting evidence from the document to generate the final answer, which is more difficult than sentence-level or phrase-level QA.
Modeling Voting for System Combination in Machine Translation
System combination is an important technique for combining the hypotheses of different machine translation systems to improve translation performance.
ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion
The release of ReCO consists of 300k questions that to our knowledge is the largest in Chinese reading comprehension.
A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction
Neural models have achieved great success on machine reading comprehension (MRC), many of which typically consist of two components: an evidence extractor and an answer predictor.
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.
Learning to Copy for Automatic Post-Editing
To better identify translation errors, our method learns the representations of source sentences and system outputs in an interactive way.
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.
Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
Although neural machine translation has made significant progress recently, how to integrate multiple overlapping, arbitrary prior knowledge sources remains a challenge.
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.
DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval
This paper concerns a deep learning approach to relevance ranking in information retrieval (IR).
