In this paper, we propose to study the problem of COURT VIEW GENeration from the fact description in a criminal case. The task aims to improve the interpretability of charge prediction systems and help automatic legal document generation. We formulate this task as a text-to-text natural language generation (NLG) problem. Sequenceto-sequence model has achieved cutting-edge performances in many NLG tasks. However, due to the non-distinctions of fact descriptions, it is hard for Seq2Seq model to generate charge-discriminative court views. In this work, we explore charge labels to tackle this issue. We propose a label-conditioned Seq2Seq model with attention for this problem, to decode court views conditioned on encoded charge labels. Experimental results show the effectiveness of our method.
Our label-conditioned Seq2Seq model steams from widely used encoder-decoder paradigm @cite_34 which has been widely used in machine translation @cite_39 @cite_20 , summarization @cite_38 @cite_40 @cite_18 @cite_0 , semantic parsing @cite_41 and paraphrase @cite_9 or other NLG problems such as product review generation @cite_23 and code generation @cite_1 @cite_14 . propose to encode image labels for visual-language models to generate justification texts for image classification. We also introduce charge labels into Seq2Seq model to improve the charge-discriminations of generated rationales. Widely used attention mechanism @cite_28 @cite_36 is applied to generate fact details more accurately.
@cite_38 @cite_18 @cite_14 @cite_41 @cite_28 @cite_9 @cite_36 @cite_1 @cite_39 @cite_0 @cite_40 @cite_23 @cite_34 @cite_20
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This work presents a general unsupervised learning method to improve the accuracy of sequence to sequence (seq2seq) models. In our method, the weights of the encoder and decoder of a seq2seq model are initialized with the pretrained weights of two language models and then fine-tuned with labeled data. We apply this method to challenging benchmarks in machine translation and abstractive summarization and find that it significantly improves the subsequent supervised models. Our main result is that pretraining improves the generalization of seq2seq models. We achieve state-of-the art results on the WMT English @math German task, surpassing a range of methods using both phrase-based machine translation and neural machine translation. Our method achieves a significant improvement of 1.3 BLEU from the previous best models on both WMT'14 and WMT'15 English @math German. We also conduct human evaluations on abstractive summarization and find that our method outperforms a purely supervised learning baseline in a statistically significant manner. This work presents a general unsupervised learning method to improve the accuracy of sequence to sequence (seq2seq) models. In our method, the weights of the encoder and decoder of a seq2seq model are initialized with the pretrained weights of two language models and then fine-tuned with labeled data. We apply this method to challenging benchmarks in machine translation and abstractive summarization and find that it significantly improves the subsequent supervised models. Our main result is that the pretraining accelerates training and improves generalization of seq2seq models, achieving state-of-the-art results on the WMT English->German task, surpassing a range of methods using both phrase-based machine translation and neural machine translation. Our method achieves an improvement of 1.3 BLEU from the previous best models on both WMT'14 and WMT'15 English->German. On summarization, our method beats the supervised learning baseline. We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoder-decoder image captioning framework for more human-like captions. Intuitively, we humans use the inductive bias to compose collocations and contextual inference in discourse. For example, when we see the relation person on bike', it is natural to replace on' with ride' and infer person riding bike on a road' even the road' is not evident. Therefore, exploiting such bias as a language prior is expected to help the conventional encoder-decoder models less likely overfit to the dataset bias and focus on reasoning. Specifically, we use the scene graph --- a directed graph ( @math ) where an object node is connected by adjective nodes and relationship nodes --- to represent the complex structural layout of both image ( @math ) and sentence ( @math ). In the textual domain, we use SGAE to learn a dictionary ( @math ) that helps to reconstruct sentences in the @math pipeline, where @math encodes the desired language prior; in the vision-language domain, we use the shared @math to guide the encoder-decoder in the @math pipeline. Thanks to the scene graph representation and shared dictionary, the inductive bias is transferred across domains in principle. We validate the effectiveness of SGAE on the challenging MS-COCO image captioning benchmark, e.g., our SGAE-based single-model achieves a new state-of-the-art @math CIDEr-D on the Karpathy split, and a competitive @math CIDEr-D (c40) on the official server even compared to other ensemble models. For machine translation to tackle discourse phenomena, models must have access to extra-sentential linguistic context. There has been recent interest in modelling context in neural machine translation (NMT), but models have been principally evaluated with standard automatic metrics, poorly adapted to evaluating discourse phenomena. In this article, we present hand-crafted, discourse test sets, designed to test the models' ability to exploit previous source and target sentences. We investigate the performance of recently proposed multi-encoder NMT models trained on subtitles for English to French. We also explore a novel way of exploiting context from the previous sentence. Despite gains using BLEU, multi-encoder models give limited improvement in the handling of discourse phenomena: 50 accuracy on our coreference test set and 53.5 for coherence cohesion (compared to a non-contextual baseline of 50 ). A simple strategy of decoding the concatenation of the previous and current sentence leads to good performance, and our novel strategy of multi-encoding and decoding of two sentences leads to the best performance (72.5 for coreference and 57 for coherence cohesion), highlighting the importance of target-side context.