Popular task-oriented dialog data sets such as MultiWOZ (Budzianowski et al. 2018) are created by providing crowd-sourced workers a goal instruction, expressed in natural language, that describes the task to be accomplished. Crowd-sourced workers play the role of a user and an agent to generate dialogs to accomplish tasks involving booking restaurant tables, making train reservations, calling a taxi etc. However, creating large crowd-sourced datasets can be time consuming and expensive. To reduce the cost associated with generating such dialog datasets, recent work has explored methods to automatically create larger datasets from small samples.In this paper, we present a data creation strategy that uses the pre-trained language model, GPT2 (Radford et al. 2018), to simulate the interaction between crowd-sourced workers by creating a user bot and an agent bot. We train the simulators using a smaller percentage of actual crowd-generated conversations and their corresponding goal instructions. We demonstrate that by using the simulated data, we achieve significant improvements in both low-resource setting as well as in over-all task performance. To the best of our knowledge we are the first to present a model for generating entire conversations by simulating the crowd-sourced data collection process

Our goal is to answer real-world tourism questions that seek Points-of-Interest (POI) recommendations. Such questions express various kinds of spatial and non-spatial constraints, necessitating a combination of textual and spatial reasoning. In response, we develop the first joint spatio-textual reasoning model, which combines geo-spatial knowledge with information in textual corpora to answer questions. We first develop a modular spatial-reasoning network that uses geo-coordinates of location names mentioned in a question, and of candidate answer POIs, to reason over only spatial constraints. We then combine our spatial-reasoner with a textual reasoner in a joint model and present experiments on a real world POI recommendation task. We report substantial improvements over existing models with-out joint spatio-textual reasoning.

In this paper we work on the recently introduced ShARC task - a challenging form of conversational QA that requires reasoning over rules expressed in natural language. Attuned to the risk of superficial patterns in data being exploited by neural models to do well on benchmark tasks (Niven and Kao 2019), we conduct a series of probing experiments and demonstrate how current state-of-the-art models rely heavily on such patterns. To prevent models from learning based on the superficial clues, we modify the dataset by automatically generating new instances reducing the occurrences of those patterns. We also present a simple yet effective model that learns embedding representations to incorporate dialog history along with the previous answers to follow-up questions. We find that our model outperforms existing methods on all metrics, and the results show that the proposed model is more robust in dealing with spurious patterns and learns to reason meaningfully.

Real world question answering can be significantly more complex than what most existing QA datasets reflect. Questions posed by users on websites, such as online travel forums, may consist of multiple sentences and not everything mentioned in a question may be relevant for finding its answer. Such questions typically have a huge candidate answer space and require complex reasoning over large knowledge corpora. We introduce the novel task of answering entity-seeking recommendation questions using a collection of reviews that describe candidate answer entities. We harvest a QA dataset that contains 48,147 paragraph-sized real user questions from travelers seeking recommendations for hotels, attractions and restaurants. Each candidate answer is associated with a collection of unstructured reviews. This dataset is challenging because commonly used neural architectures for QA are prohibitively expensive for a task of this scale. As a solution, we design a scalable cluster-select-rerank approach. It first clusters text for each entity to identify exemplar sentences describing an entity. It then uses a scalable neural information retrieval (IR) module to subselect a set of potential entities from the large candidate set. A reranker uses a deeper attention-based architecture to pick the best answers from the selected entities. This strategy performs better than a pure IR or a pure attention-based reasoning approach yielding nearly 10% relative improvement in Accuracy@3 over both approaches.

We introduce the first system towards the novel task of answering complex multi-sentence recommendation questions in the tourism domain. Our solution uses a pipeline of two modules: question understanding and answering. For question understanding, we define an SQL-like query language that captures the semantic intent of a question; it supports operators like subset, negation, preference and similarity, which are often found in recommendation questions. We train and compare traditional CRFs as well as bidirectional LSTM-based models for converting a question to its semantic representation. We extend these models to a semi-supervised setting with partially labeled sequences gathered through crowdsourc-ing. We find that our best model performs semi-supervised training of BiDiL-STM CRF with hand-designed features and CCM(Chang et al., 2007) constraints. Finally, in an end to end QA system, our answering component converts our question representation into queries fired on underlying knowledge sources. Our experiments on two different answer corpora demonstrate that our system can significantly outperform baselines with up to 20 pt higher accuracy and 17 pt higher recall.