We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+ annotated SQL queries, obtained from a Wizard-of-Oz (WOZ) collection of 3k dialogues querying 200 complex DBs spanning 138 domains. Each dialogue simulates a real-world DB query scenario with a crowd worker as a user exploring the DB and a SQL expert retrieving answers with SQL, clarifying ambiguous questions, or otherwise informing of unanswerable questions. When user questions are answerable by SQL, the expert describes the SQL and execution results to the user, hence maintaining a natural interaction flow. CoSQL introduces new challenges compared to existing task-oriented dialogue datasets: (1) the dialogue states are grounded in SQL, a domain-independent executable representation, instead of domain-specific slotvalue pairs, and (2) because testing is done on unseen databases, success requires generalizing to new domains. CoSQL includes three tasks: SQL-grounded dialogue state tracking, response generation from query results, and user dialogue act prediction. We evaluate a set of strong baselines for each task and show that CoSQL presents significant challenges for future research. The dataset, baselines, and leaderboard will be released at https:// yale-lily.github.io/cosql.

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We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our minimal feature set into the dynamic-programming framework of Huang and Sagae (2010) and Kuhlmann et al. (2011) to produce the first implementation of worst-case Opn 3 q exact decoders for arc-hybrid and arceager transition systems. With our minimal features, we also present Opn 3 q global training methods. Finally, using ensembles including our new parsers, we achieve the best unlabeled attachment score reported (to our knowledge) on the Chinese Treebank and the "second-best-in-class" result on the English Penn Treebank.

We focus on the cross-domain contextdependent text-to-SQL generation task. Based on the observation that adjacent natural language questions are often linguistically dependent and their corresponding SQL queries tend to overlap, we utilize the interaction history by editing the previous predicted query to improve the generation quality. Our editing mechanism views SQL as sequences and reuses generation results at the token level in a simple manner. It is flexible to change individual tokens and robust to error propagation. Furthermore, to deal with complex table structures in different domains, we employ an utterance-table encoder and a table-aware decoder to incorporate the context of the user utterance and the table schema. We evaluate our approach on the SParC dataset and demonstrate the benefit of editing compared with the state-of-the-art baselines which generate SQL from scratch. Our code is available at https://github.com/ ryanzhumich/sparc_atis_pytorch.

With the increase of wind energy conversion system (WECS) capacity, conventional two-level voltage source converters tend to be replaced gradually by multilevel neutral-point (NP)-clamped converters. In this study, the topology of a boost threelevel (TL) chopper on the front of a TL diode-clamped inverter is used for direct-driven WECSs. The switch-signal phase delay control (SSPDC) is proposed for NP potential balancing of TL inverter based on the characteristics of boost TL chopper. As the boost chopper is also used for maximum power point tracking, the controller with dual PI regulators is designed for the chopper. Parameters of the two PI regulators are determined according to the state space averaging model of the boost TL chopper using SSPDC. In addition, the proposed NP balancing method is compared with two methods based on redundant vector selection. The validity of the proposed method is verified by analysis and simulation results.Index Terms-Boost three-level (TL) chopper, direct-driven wind energy system, neutral-point (NP) potential balancing, redundant vector selection (RVS), switch-signal phase delay control (SSPDC), TL diode-clamped inverter.