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In this paper we present the Exemplar Encoder-Decoder network (EED), a novel conversation model that learns to utilize similar examples from training data to generate responses. Similar conversation examples (context-response pairs) from training data are retrieved using a traditional TF-IDF based retrieval model. The retrieved responses are used to create exemplar vectors that are used by the decoder to generate the response. The contribution of each retrieved response is weighed by the similarity of corresponding context with the input context. We present detailed experiments on two large data sets and find that our method outperforms state of the art sequence to sequence generative models on several recently proposed evaluation metrics. We also observe that the responses generated by the proposed EED model are more informative and diverse compared to existing state-of-the-art method.

Recent end-to-end task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. Current work makes simplifying assumptions about the structure of the knowledge base (such as the use of triples to represent knowledge) and combines dialog utterances (context), as well as, knowledge base (KB) results, as part of the same memory. This causes an explosion in the memory size, and makes reasoning over memory, harder. In addition, such a memory design forces hierarchical properties of the data to be fit into a triple structure of memory. This requires the memory reader to learn how to infer relationships across otherwise connected attributes. In this paper we relax the strong assumptions made by existing architectures and use separate memories for modeling dialog context and KB results. Instead of using triples to store KB results, we introduce a novel multilevel memory architecture consisting of cells for each query and their corresponding results. The multi-level memory first addresses queries, followed by results and finally each key-value pair within a result. We conduct detailed experiments on three publicly available task oriented dialog data sets and we find that our method conclusively outperforms current state-of-the-art models. We report a 15-25% increase in both entity F1 and BLEU scores.

Neural Conversational QA tasks like ShARC require systems to answer questions based on the contents of a given passage. On studying recent state-of-the-art models on the ShARC QA task, we found indications that the models learn spurious clues/patterns in the dataset. Furthermore, we show that a heuristic-based program designed to exploit these patterns can have performance comparable to that of the neural models. In this paper we share our findings about four types of patterns found in the ShARC corpus and describe how neural models exploit them. Motivated by the aforementioned findings, we create and share a modified dataset that has fewer spurious patterns, consequently allowing models to learn better.