Neural Response Generation via GAN with an Approximate Embedding Layer

Xu, Zhen; Liu, Bingquan; Wang, Baoxun; Sun, Chengjie; Wang, Xiaolong; Wang, Zhuoran; Chen, Qi

doi:10.18653/v1/d17-1065

Cited by 83 publications

(85 citation statements)

References 18 publications

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Order By: Relevance

“…To evaluate our proposed method, we employ BLEU [19] to measure the quality of generated sentence by computing overlapping lexical units (e.g., unigram, bigram) with the reference sentence. We also consider three embedding-based metrics [6] (including Embedding Average, Embedding Greedy and Embedding Extreme) to evaluate our model, following several recent studies on text generation [24,30,35]. These three metrics compute the semantic similarity between the generated and reference answer according to the word embedding.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Product-Aware Answer Generation in E-Commerce Question-Answering

Gao

Zhang

Zhao

et al. 2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

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In e-commerce portals, generating answers for product-related questions has become a crucial task. In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. Unlike existing question-answering problems, answer generation in e-commerce confronts three main challenges: (1) Reviews are informal and noisy; (2) joint modeling of reviews and key-value product attributes is challenging; (3) traditional methods easily generate meaningless answers. To tackle above challenges, we propose an adversarial learning based model, named PAAG, which is composed of three components: a questionaware review representation module, a key-value memory network encoding attributes, and a recurrent neural network as a sequence generator. Specifically, we employ a convolutional discriminator to distinguish whether our generated answer matches the facts. To extract the salience part of reviews, an attention-based review reader is proposed to capture the most relevant words given the question.Conducted on a large-scale real-world e-commerce dataset, our extensive experiments verify the effectiveness of each module in our proposed model. Moreover, our experiments show that our model achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.

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Section: Evaluation Metricsmentioning

confidence: 99%

Product-Aware Answer Generation in E-Commerce Question-Answering

Gao

Zhang

Zhao

et al. 2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

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show abstract

“…Dataset Following the previous studies (Vinyals and Le, 2015;Li et al, 2017;Xu et al, 2017), we choose the widely-used OpenSubtitles (Tiedemann, 2009) dataset to evaluate different methods. The OpenSubtitles dataset contains movie scripts organized by characters, where we follow Li et al (2016b) to retain subtitles containing 5-50 words.…”

Section: Open-domain Dialogue Learningmentioning

confidence: 99%

“…We argue that in a meaningful and coherent dialogue, the change of utterance order will lead to a low-quality dialogue. However, most existing neural-based dialogue systems either encode the full dialogue history (Li et al, 2017;Xu et al, 2017) or only the current utterance (Liu and Lane, 2018). None of them explicitly models the sequential order and studies its criticality to the dialogue learning problem.…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Dialogue Learning

Wu¹,

Wang²,

Wang³

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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The sequential order of utterances is often meaningful in coherent dialogues, and the order changes of utterances could lead to lowquality and incoherent conversations. We consider the order information as a crucial supervised signal for dialogue learning, which, however, has been neglected by many previous dialogue systems. Therefore, in this paper, we introduce a self-supervised learning task, inconsistent order detection, to explicitly capture the flow of conversation in dialogues. Given a sampled utterance pair triple, the task is to predict whether it is ordered or misordered. Then we propose a samplingbased self-supervised network SSN to perform the prediction with sampled triple references from previous dialogue history. Furthermore, we design a joint learning framework where SSN can guide the dialogue systems towards more coherent and relevant dialogue learning through adversarial training. We demonstrate that the proposed methods can be applied to both open-domain and taskoriented dialogue scenarios, and achieve the new state-of-the-art performance on the Open-Subtitiles and Movie-Ticket Booking datasets.

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“…We follow previous question generation work (Xu et al, 2017;Du et al, 2017) to use BLEU 5 (Papineni et al, 2002) and ROUGE-L (Lin, 2004) to measure the relevance between the generated question and the ground-truth one. To evaluate the diversity of the generated questions, we follow (Li et al, 2016a) to calculate Dist-n (n=1,2), which is the proportion of unique n-grams over the total number of n-grams in the generated questions for all passages, and to use the Ent-n (n=4) metric, which reflects how evenly the n-gram distribution is over all generated questions.…”

Section: Metricsmentioning

confidence: 99%

Reinforced Dynamic Reasoning for Conversational Question Generation

Pan¹,

Li²,

Yao³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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This paper investigates a new task named Conversational Question Generation (CQG) which is to generate a question based on a passage and a conversation history (i.e., previous turns of question-answer pairs). CQG is a crucial task for developing intelligent agents that can drive question-answering style conversations or test user understanding of a given passage. Towards that end, we propose a new approach named Reinforced Dynamic Reasoning (ReDR) network, which is based on the general encoder-decoder framework but incorporates a reasoning procedure in a dynamic manner to better understand what has been asked and what to ask next about the passage. To encourage producing meaningful questions, we leverage a popular question answering (QA) model to provide feedback and fine-tune the question generator using a reinforcement learning mechanism. Empirical results on the recently released CoQA dataset demonstrate the effectiveness of our method in comparison with various baselines and model variants. Moreover, to show the applicability of our method, we also apply it to create multiturn question-answering conversations for passages in SQuAD.

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Neural Response Generation via GAN with an Approximate Embedding Layer

Cited by 83 publications

References 18 publications

Product-Aware Answer Generation in E-Commerce Question-Answering

Product-Aware Answer Generation in E-Commerce Question-Answering

Self-Supervised Dialogue Learning

Reinforced Dynamic Reasoning for Conversational Question Generation

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