You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We propose minimum risk training for end-to-end neural machine translation. Unlike conventional maximum likelihood estimation, minimum risk training is capable of optimizing model parameters directly with respect to arbitrary evaluation metrics, which are not necessarily differentiable. Experiments show that our approach achieves significant improvements over maximum likelihood estimation on a state-of-the-art neural machine translation system across various languages pairs. Transparent to architectures, our approach can be applied to more neural networks and potentially benefit more NLP tasks.

Cited Abstract(s):
[1]: Many natural language processing applications use language models to generate text. These models are typically trained to predict the next word in a sequence, given the previous words and some context such as an image. However, at test time the model is expected to generate the entire sequence from scratch. This discrepancy makes generation brittle, as errors may accumulate along the way. We address this issue by proposing a novel sequence level training algorithm that directly optimizes the metric used at test time, such as BLEU or ROUGE. On three different tasks, our approach outperforms several strong baselines for greedy generation. The method is also competitive when these baselines employ beam search, while being several times faster.
The Mixed Incremental Cross-Entropy Reinforce (MIXER) algorithm [1] is in spirit closest to our work. Building on the REINFORCE algorithm proposed by Williams , MIXER allows incremental learning and the use of hybrid loss function that combines both REINFORCE and cross-entropy. The major difference is that leverage reinforcement learning while our work resorts to minimum risk training. In addition, MIXER only samples one candidate to calculate reinforcement reward while MRT generates multiple samples to calculate the expected risk. Figure indicates that multiple samples potentially increases MRT's capability of discriminating between diverse candidates and thus benefit translation quality. Our experiments confirm 's finding that taking evaluation metrics into account when optimizing model parameters does help to improve sentence-level text generation.