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

Gao, Shen; Zhang, Ren; Zhao, Yihong; Zhao, Dongyan; Yin, Dawei; Yan, Rui

doi:10.1145/3289600.3290992

Cited by 69 publications

(72 citation statements)

References 62 publications

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“…Visual dialog. Visual dialog extends the single turn dialog task [14,31] in VQA to a multi-turn one, where later questions may be related to former question-answer pairs. To solve this task, [26] transfers knowledge from a pre-trained discriminative network to a generative network with an RNN encoder, using a perceptual loss.…”

Section: Related Workmentioning

confidence: 99%

Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog

Gao

Chen

Liu

et al. 2020

Proceedings of the Web Conference 2020

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Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching text labels of stickers with previous utterances. However, due to their large quantities, it is impractical to require text labels for the all stickers. Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context history without any external labels. Two main challenges are confronted in this task. One is to learn semantic meaning of stickers without corresponding text labels. Another challenge is to jointly model the candidate sticker with the multi-turn dialog context. To tackle these challenges, we propose a sticker response selector (SRS) model. Specifically, SRS first employs a convolutional based sticker image encoder and a self-attention based multi-turn dialog encoder to obtain the representation of stickers and utterances. Next, deep interaction network is proposed to conduct deep matching between the sticker with each utterance in the dialog history. SRS then learns the short-term and long-term dependency between all interaction results by a fusion network to output the the final matching score. To evaluate our proposed method, we collect a large-scale realworld dialog dataset with stickers from one of the most popular online chatting platform. Extensive experiments conducted on this dataset show that our model achieves the state-of-the-art performance for all commonly-used metrics. Experiments also verify the * Equal contribution. Ordering is decided by a coin flip. Work performed during an internship at IIAI. † WICT is the abbreviation of Wangxuan Institute of Computer Technology. effectiveness of each component of SRS. To facilitate further research in sticker selection field, we release this dataset of 340K multi-turn dialog and sticker pairs 1 .

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Section: Related Workmentioning

confidence: 99%

Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog

Gao

Chen

Liu

et al. 2020

Proceedings of the Web Conference 2020

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“…Recent studies have noticed the problem and focused on generating appropriate seller responses by integrating external information, e.g., product attributes and titles, into single-turn dialogue generation Gao et al, 2019). However, they are difficult to generalize in reality because of limited materials on hand and different scenarios.…”

Section: Ground Truthmentioning

confidence: 99%

Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue

Zhang¹,

Song²,

Kang³

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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As an important research topic, customer service dialogue generation tends to generate generic seller responses by leveraging current dialogue information. In this study, we propose a novel and extensible dialogue generation method by leveraging sellers' historical dialogue information, which can be both accessible and informative. By utilizing innovative historical dialogue representation learning and historical dialogue selection mechanism, the proposed model is capable of detecting most related responses from sellers' historical dialogues, which can further enhance the current dialogue generation quality. Unlike prior dialogue generation efforts, we treat each seller's historical dialogues as a list of Customer-Seller utterance pairs and allow the model to measure their different importance, and copy words directly from most relevant pairs. Extensive experimental results show that the proposed approach can generate highquality responses that cater to specific sellers' characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset.

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“…Mitra (2017) proposed a seq2seq-based model that learns alignment between a question and passage words to produce rich question-aware passage representation by which it directly decodes an answer. Gao et al (2019) focused on product-aware answer generation based on large-scale unlabeled e-commerce reviews and product attributes. Furthermore, natural answer generation can be refor-mulated as query-focused summarization which is addressed by Nema et al (2017).…”

Section: Related Workmentioning

confidence: 99%

Incorporating External Knowledge into Machine Reading for Generative Question Answering

Bi¹,

Wu²,

Yan³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Commonsense and background knowledge is required for a QA model to answer many nontrivial questions. Different from existing work on knowledge-aware QA, we focus on a more challenging task of leveraging external knowledge to generate answers in natural language for a given question with context.In this paper, we propose a new neural model, Knowledge-Enriched Answer Generator (KEAG), which is able to compose a natural answer by exploiting and aggregating evidence from all four information sources available: question, passage, vocabulary and knowledge. During the process of answer generation, KEAG adaptively determines when to utilize symbolic knowledge and which fact from the knowledge is useful. This allows the model to exploit external knowledge that is not explicitly stated in the given text, but that is relevant for generating an answer. The empirical study on public benchmark of answer generation demonstrates that KEAG improves answer quality over models without knowledge and existing knowledge-aware models, confirming its effectiveness in leveraging knowledge.

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

Cited by 69 publications

References 62 publications

Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog

Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog

Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue

Incorporating External Knowledge into Machine Reading for Generative Question Answering

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