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.
More recently, our approach has been successfully applied to summarization @cite_7 . They optimize neural networks for headline generation with respect to ROUGE @cite_18 and also achieve significant improvements, confirming the effectiveness and applicability of our approach.
@cite_18 @cite_7
1598469373 2250968833 2787752238 2252239149
In this paper we explore multiple features for extractive automatic summarization using machine learning. They account for SuPor-2 features, a supervised summarizer for Brazilian Portuguese, and graph-based features mirroring complex networks measures. Four different classifiers and automatic feature selection are explored. ROUGE is used for assessment of single-document summarization of news texts. We present an approach for extractive single-document summarization. Our approach is based on a weighted graphical representation of documents obtained by topic modeling. We optimize importance, coherence and non-redundancy simultaneously using ILP. We compare ROUGE scores of our system with state-of-the-art results on scientific articles from PLOS Medicine and on DUC 2002 data. Human judges evaluate the coherence of summaries generated by our system in comparision to two baselines. Our approach obtains competitive performance. Automatic headline generation is related to automatic text summarization and it is useful to solve information flood problems. This paper aims at generating a headline using a recurrent neural network which is based on a machine translation approach. Our headline generator consists of an encoder and a decoder and they are constructed with Long Short Term Memory, which is one of recurrent neural networks. The encoder constructs distributed representation from the first sentence in an article and the decoder generated headlines from the distributed representation. In our experiments, we confirmed that our proposed method could generate appropriate headlines but in some articles this method generates meaningless headlines. The results show that our proposed method is superior to another approach, statistical machine translation from the viewpoint of ROUGE, which is an evaluation score of automatic text summarization. Furthermore, we could find that using an input sentence in reverse order improves the quality of headline generation. We present a dual decomposition framework for multi-document summarization, using a model that jointly extracts and compresses sentences. Compared with previous work based on integer linear programming, our approach does not require external solvers, is significantly faster, and is modular in the three qualities a summary should have: conciseness, informativeness, and grammaticality. In addition, we propose a multi-task learning framework to take advantage of existing data for extractive summarization and sentence compression. Experiments in the TAC2008 dataset yield the highest published ROUGE scores to date, with runtimes that rival those of extractive summarizers.