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While conversing with chatbots, humans typically tend to ask many questions,a significant portion of which can be answered by referring to large-scaleknowledge graphs (KG). While Question Answering (QA) and dialog systems havebeen studied independently, there is a need to study them closely to evaluatesuch real-world scenarios faced by bots involving both these tasks. Towardsthis end, we introduce the task of Complex Sequential QA which combines the twotasks of (i) answering factual questions through complex inferencing over arealistic-sized KG of millions of entities, and (ii) learning to conversethrough a series of coherently linked QA pairs. Through a labor intensivesemi-automatic process, involving in-house and crowdsourced workers, we createda dataset containing around 200K dialogs with a total of 1.6M turns. Further,unlike existing large scale QA datasets which contain simple questions that canbe answered from a single tuple, the questions in our dialogs require a largersubgraph of the KG. Specifically, our dataset has questions which requirelogical, quantitative, and comparative reasoning as well as their combinations.This calls for models which can: (i) parse complex natural language questions,(ii) use conversation context to resolve coreferences and ellipsis inutterances, (iii) ask for clarifications for ambiguous queries, and finally(iv) retrieve relevant subgraphs of the KG to answer such questions. However,our experiments with a combination of state of the art dialog and QA modelsshow that they clearly do not achieve the above objectives and are inadequatefor dealing with such complex real world settings. We believe that this newdataset coupled with the limitations of existing models as reported in thispaper should encourage further research in Complex Sequential QA.

Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph