MultiWOZ is a recently-released multidomain dialogue dataset spanning 7 distinct domains and containing over 10000 dialogues, one of the largest resources of its kind to-date. Though an immensely useful resource, while building different classes of dialogue state tracking models using MultiWOZ, we detected substantial errors in the state annotations and dialogue utterances which negatively impacted the performance of our models. In order to alleviate this problem, we use crowdsourced workers to fix the state annotations and utterances in the original version of the data. Our correction process results in changes to over 32% of state annotations across 40% of the dialogue turns. In addition, we fix 146 dialogue utterances throughout the dataset focusing in particular on addressing slot value errors represented within the conversations. We then benchmark a number of state-of-the-art dialogue state tracking models on this new Mul-tiWOZ 2.1 dataset and show joint state tracking performance on the corrected state annotations. We are publicly releasing MultiWOZ 2.1 to the community, hoping that this dataset resource will allow for more effective dialogue state tracking models to be built in the future.

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Dialog state tracking is used to estimate the current belief state of a dialog given all the preceding conversation. Machine reading comprehension, on the other hand, focuses on building systems that read passages of text and answer questions that require some understanding of passages. We formulate dialog state tracking as a reading comprehension task to answer the question what is the state of the current dialog? after reading conversational context. In contrast to traditional state tracking methods where the dialog state is often predicted as a distribution over a closed set of all the possible slot values within an ontology, our method uses a simple attention-based neural network to point to the slot values within the conversation. Experiments on MultiWOZ-2.0 cross-domain dialog dataset show that our simple system can obtain similar accuracies compared to the previous more complex methods. By exploiting recent advances in contextual word embeddings, adding a model that explicitly tracks whether a slot value should be carried over to the next turn, and combining our method with a traditional joint state tracking method that relies on closed set vocabulary, we can obtain a joint-goal accuracy of 47.33% on the standard test split, exceeding current state-of-the-art by 11.75%**.

Perovskite-based solar cells have attracted much recent research interest with efficiency approaching 20%. While various combinations of material parameters and processing conditions are attempted for improved performance, there is still a lack of understanding in terms of the basic device physics and functional parameters that control the efficiency. Here we show that perovskite-based solar cells have two universal features: an ideality factor close to two and a space-charge-limited current regime. Through detailed numerical modeling, we identify the mechanisms that lead to these universal features. Our model predictions are supported by experimental results on solar cells fabricated at five different laboratories using different materials and processing conditions. Indeed, this work unravels the fundamental operation principle of perovskite-based solar cells, suggests ways to improve the eventual performance, and serves as a benchmark to which experimental results from various laboratories can be compared.

Fast and efficient cleanup of crude oil spills is still a global challenge because most of the crude oils are highly viscous and lowly fluid. Herein, a kind of polydimethylsiloxane-decorated wood carbon sponges (PDMS@WCS) with desirable compressibility and hydrophobicity for the fast adsorption and enhanced recovery of crude oil via the promotion of Joule-heating and photothermal effect is reported. Moreover, the PDMS@WCS can be compressed and released at a constant strain of 50% for over times without structural damage due to the protection of PDMS coating. Thus, the adsorbed crude oil can be facilely excluded from PDMS@WCS under external pressure to show enhanced recovery.