We introduce a new task named Story Ending Generation (SEG), which aims at generating a coherent story ending from a sequence of story plot. We propose a framework consisting of a Generator and a Reward Manager for this task. The Generator follows the pointer-generator network with coverage mechanism to deal with out-of-vocabulary (OOV) and repetitive words. Moreover, a mixed loss method is introduced to enable the Generator to produce story endings of high semantic relevance with story plots. In the Reward Manager, the reward is computed to fine-tune the Generator with policy-gradient reinforcement learning (PGRL). We conduct experiments on the recently-introduced ROCStories Corpus. We evaluate our model in both automatic evaluation and human evaluation. Experimental results show that our model exceeds the sequence-to-sequence baseline model by 15.75 and 13.57 in terms of CIDEr and consistency score respectively.
Encoder-decoder framework, which uses neural networks as encoder and decoder, was first proposed in machine translation @cite_7 @cite_12 and has been widely used in NLG tasks. The encoder reads and encodes a source sentence into a fixed-length vector, then the decoder outputs a new sequence from the encoded vector. Attention mechanism @cite_3 extends the basic encoder-decoder framework by assigning different weights to input words when generating each target word. @cite_32 , @cite_22 and @cite_18 apply attention-based encoder-decoder model to text summarization. @cite_0 and @cite_1 develop new and effective attentive models for machine reading comprehension.
@cite_18 @cite_22 @cite_7 @cite_1 @cite_32 @cite_3 @cite_0 @cite_12
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The encoder-decoder framework has achieved promising progress for many sequence generation tasks, including machine translation, text summarization, dialog system, image captioning, etc. Such a framework adopts an one-pass forward process while decoding and generating a sequence, but lacks the deliberation process: A generated sequence is directly used as final output without further polishing. However, deliberation is a common behavior in human's daily life like reading news and writing papers articles books. In this work, we introduce the deliberation process into the encoder-decoder framework and propose deliberation networks for sequence generation. A deliberation network has two levels of decoders, where the first-pass decoder generates a raw sequence and the second-pass decoder polishes and refines the raw sentence with deliberation. Since the second-pass deliberation decoder has global information about what the sequence to be generated might be, it has the potential to generate a better sequence by looking into future words in the raw sentence. Experiments on neural machine translation and text summarization demonstrate the effectiveness of the proposed deliberation networks. On the WMT 2014 English-to-French translation task, our model establishes a new state-of-the-art BLEU score of 41.5. In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases. In this paper, we propose a novel neural network model called RNN Encoder‐ Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder‐Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases. We build a multi-source machine translation model and train it to maximize the probability of a target English string given French and German sources. Using the neural encoder-decoder framework, we explore several combination methods and report up to +4.8 Bleu increases on top of a very strong attention-based neural translation model.