Conversational Query Understanding Using Sequence to Sequence Modeling

Ren, Gary; Ni, Xiaochuan; Malik, Manish; Ke, Qifa

doi:10.1145/3178876.3186083

Cited by 41 publications

(35 citation statements)

References 24 publications

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“…Question completion approaches [17,26,28] target this setting by attempting to create full-fledged interrogatives from partial utterances while being independent of the answering resource, but suffer in situations without training pairs and with ad hoc styles. Nevertheless, we try to compare with this line of thought, and show that such completion may not be necessary if the underlying KG can be properly exploited.…”

Section: Related Workmentioning

confidence: 99%

“…$15.00 https://doi.org/10.1145/nnnnnnn.nnnnnnn not always expressed in well-formed and self-contained questions for one-shot processing. Quite often, users issue a series of followup questions to explore a topic [12,30], analogous to search sessions [28]. A major challenge in such conversational QA settings is that follow-up questions are often incomplete, with entities or predicates not spelled out, and use of ungrammatical phrases.…”

Section: Introduction 1motivationmentioning

confidence: 99%

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Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion

Christmann

Roy

Abujabal

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

118

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Fact-centric information needs are rarely one-shot; users typically ask follow-up questions to explore a topic. In such a conversational setting, the user's inputs are often incomplete, with entities or predicates left out, and ungrammatical phrases. This poses a huge challenge to question answering (QA) systems that typically rely on cues in full-fledged interrogative sentences. As a solution, we develop Convex: an unsupervised method that can answer incomplete questions over a knowledge graph (KG) by maintaining conversation context using entities and predicates seen so far and automatically inferring missing or ambiguous pieces for follow-up questions. The core of our method is a graph exploration algorithm that judiciously expands a frontier to find candidate answers for the current question. To evaluate Convex, we release ConvQuestions, a crowdsourced benchmark with 11, 200 distinct conversations from five different domains. We show that Convex: (i) adds conversational support to any stand-alone QA system, and (ii) outperforms state-of-the-art baselines and question completion strategies.

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Section: Related Workmentioning

confidence: 99%

Section: Introduction 1motivationmentioning

confidence: 99%

Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion

Christmann

Roy

Abujabal

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

118

View full text Add to dashboard Cite

show abstract

“…Inspired by query formulation [1,8], we propose a novel contextual query reformulation (CQR) method for general CQU (Figure 1). Outputs of CQR are standalone queries freed from contextual dependency.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, CQR is an independent module naturally adapting to downstream technologies. Instead of reformulating queries end-to-end [7,8,11], intermediate actions are designed to capture syntactic regularities of conversations, and new queries are synthesized from the actions. The regularities are imposed across domains by natural language itself (domain-independent), and the actions essentially decouple them from domain-specific entity information.…”

Section: Introductionmentioning

confidence: 99%

Learning to Generate Reformulation Actions for Scalable Conversational Query Understanding

Zhu²,

Geng³

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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The ability of conversational query understanding (CQU) is indispensable to multi-turn QA. However, existing methods are datadriven and expensive to extend to new conversation domains, or under specific frameworks and hard to apply to other underlying QA technologies. We propose a novel contextual query reformulation (CQR) module based on reformulation actions for general CQU. The actions are domain-independent and scalable, since they capture syntactic regularities of conversations. For action generation, we propose a multi-task learning framework enhanced by coreference resolution, and introduce grammar constraints into the decoding process. Then CQR synthesizes standalone queries based on the actions, which naturally adapts to original downstream technologies. Experiments on different CQU datasets suggest that action-based methods substantially outperform direct reformulation, and the proposed model performs the best among the methods.

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“…Hence, simple slot based state tracking may not be able to handle complex situations in E-commerce conversational search. Last but not the least, it is important to summarize current user intention in a query and feed it to search engine in conversational search [2,7]. We call the query that represents current user intention "internal query".…”

Section: Introductionmentioning

confidence: 99%

Query Tracking for E-commerce Conversational Search

Yang

Gong

Chen

2018

Proceedings of the 27th ACM International Conference on Information and Knowledge Management

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With the development of dialog techniques, conversational search has attracted more and more attention as it enables users to interact with the search engine in a natural and efficient manner. However, comparing with the natural language understanding in traditional task-oriented dialog which focuses on slot filling and tracking, the query understanding in E-commerce conversational search is quite different and more challenging due to more diverse user expressions and complex intentions. In this work, we define the real-world problem of query tracking in E-commerce conversational search, in which the goal is to update the internal query after each round of interaction. We also propose a self attention based neural network to handle the task in a machine comprehension perspective. Further more we build a novel E-commerce query tracking dataset from an operational E-commerce Search Engine, and experimental results on this dataset suggest that our proposed model outperforms several baseline methods by a substantial gain for Exact Match accuracy and F1 score, showing the potential of machine comprehension like model for this task. CCS CONCEPTS• Information systems → Information retrieval query processing; Users and interactive retrieval; KEYWORDS query tracking, machine comprehension, neural network, conversational search ACM Reference Format:

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Conversational Query Understanding Using Sequence to Sequence Modeling

Cited by 41 publications

References 24 publications

Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion

Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion

Learning to Generate Reformulation Actions for Scalable Conversational Query Understanding

Query Tracking for E-commerce Conversational Search

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