Reinforced Dynamic Reasoning for Conversational Question Generation

Pan, Boyuan; Li, Hao; Yao, Zhiliang; Sun, Huan

doi:10.48550/arxiv.1907.12667

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…4.ASs2s [12] : The use of answer information is added on the basis of the s2s architecture. The model decodes questions from paragraph encoders and answer encoders based on keyword networks.…”

Section: Baselinesmentioning

confidence: 99%

A question generation model based on semantic enhancement and reinforcement learning

Chang

2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

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The purpose of the Question Generation (QG) task is to automatically generate a reasonable and relevant question for the given document, the goal of this paper is to improve the performance of the question generation model and thus generate higher quality questions. This paper mainly focuses on the neural network model of Seq2Seq architecture and aims to two problems existed: exposure bias caused by the difference between the training and testing processes of the current Seq2Seq architecture model, and insufficient utilization of semantic information of the input documents. This paper proposes a question generation method based on semantic enhancement and reinforcement learning, which enhances the document representation through semantic graph; Add the reinforcement learning framework, use three special indicators for question generation tasks to fine-tune the model parameters during the training process to improve the performance of the model. The experiments’ results on show that this method effectively improves the quality of generating questions.

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Section: Baselinesmentioning

confidence: 99%

A question generation model based on semantic enhancement and reinforcement learning

Chang

2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

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“…Multi-hop QG requires aggregating several scattered evidence spans from multiple paragraphs, and reasoning over them to generate answer-related, factual-coherent questions. It can serve as an essential component in education systems (Heilman and Smith, 2010;Lindberg et al, 2013;Yao et al, 2018), or be applied in intelligent virtual assistant systems (Shum et al, 2018;Pan et al, 2019). It can also combine with question answering (QA) models as dual tasks to boost QA systems with reasoning ability (Tang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Multi-hop Question Generation with Graph Convolutional Network

Su,

Xu,

Dai

et al. 2020

Preprint

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Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs. It is a more challenging yet under-explored task compared to conventional single-hop QG, where the questions are generated from the sentence containing the answer or nearby sentences in the same paragraph without complex reasoning. To address the additional challenges in multi-hop QG, we propose Multi-Hop Encoding Fusion Network for Question Generation (MulQG), which does context encoding in multiple hops with Graph Convolutional Network and encoding fusion via an Encoder Reasoning Gate. To the best of our knowledge, we are the first to tackle the challenge of multi-hop reasoning over paragraphs without any sentence-level information. Empirical results on HotpotQA dataset demonstrate the effectiveness of our method, in comparison with baselines on automatic evaluation metrics. Moreover, from the human evaluation, our proposed model is able to generate fluent questions with high completeness and outperforms the strongest baseline by 20.8% in the multihop evaluation. The code is publicly available at https://github.com/HLTCHKUST/MulQG.

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Bi-Decoder Augmented Network for Neural Machine Translation

et al. 2020

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Neural Machine Translation (NMT) has become a popular technology in recent years, and the encoderdecoder framework is the mainstream among all the methods. It's obvious that the quality of the semantic representations from encoding is very crucial and can significantly affect the performance of the model. However, existing unidirectional source-to-target architectures may hardly produce a language-independent representation of the text because they rely heavily on the specific relations of the given language pairs. To alleviate this problem, in this paper, we propose a novel Bi-Decoder Augmented Network (BiDAN) for the neural machine translation task. Besides the original decoder which generates the target language sequence, we add an auxiliary decoder to generate back the source language sequence at the training time. Since each decoder transforms the representations of the input text into its corresponding language, jointly training with two target ends can make the shared encoder has the potential to produce a language-independent semantic space. We conduct extensive experiments on several NMT benchmark datasets and the results demonstrate the effectiveness of our proposed approach.

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Reinforced Dynamic Reasoning for Conversational Question Generation

Cited by 4 publications

References 25 publications

A question generation model based on semantic enhancement and reinforcement learning

A question generation model based on semantic enhancement and reinforcement learning

Multi-hop Question Generation with Graph Convolutional Network

Bi-Decoder Augmented Network for Neural Machine Translation

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