Large repositories of products, patents and scientific papers offer an opportunity for building systems that scour millions of ideas and help users discover inspirations. However, idea descriptions are typically in the form of unstructured text, lacking key structure that is required for supporting creative innovation interactions. Prior work has explored idea representations that were either limited in expressivity, required significant manual effort from users, or dependent on curated knowledge bases with poor coverage. We explore a novel representation that automatically breaks up products into fine-grained functional aspects capturing the purposes and mechanisms of ideas, and use it to support important creative innovation interactions: functional search for ideas, and exploration of the design space around a focal problem by viewing related problem perspectives pooled from across many products. In user studies, our approach boosts the quality of creative search and inspirations, substantially outperforming strong baselines by 50-60%.

Can we teach natural language understanding models to track their beliefs through intermediate points in text?We propose a representation learning framework called breakpoint modeling that allows for learning of this type.Given any text encoder and data marked with intermediate states (breakpoints) along with corresponding textual queries viewed as true/false propositions (i.e., the candidate beliefs of a model, consisting of information changing through time) our approach trains models in an efficient and end-to-end fashion to build intermediate representations that facilitate teaching and direct querying of beliefs at arbitrary points alongside solving other end tasks.To show the benefit of our approach, we experiment with a diverse set of NLU tasks including relational reasoning on CLUTRR and narrative understanding on bAbI.Using novel belief prediction tasks for both tasks, we show the benefit of our main breakpoint transformer, based on T5, over conventional representation learning approaches in terms of processing efficiency, prediction accuracy and prediction consistency, all with minimal to no effect on corresponding QA endtasks.To show the feasibility of incorporating our belief tracker into more complex reasoning pipelines, we also obtain SOTA performance on the three-tiered reasoning challenge for the TRIP benchmark (around 23-32% absolute improvement on Tasks 2-3). 1

We develop Process Execution Graphs (PEG), a document-level representation of real-world wet lab biochemistry protocols, addressing challenges such as cross-sentence relations, long-range coreference, grounding, and implicit arguments.We manually annotate PEGs in a corpus of complex lab protocols with a novel interactive textual simulator that keeps track of entity traits and semantic constraints during annotation.We use this data to develop graph-prediction models, finding them to be good at entity identification and local relation extraction, while our corpus facilitates further exploration of challenging long-range relations.1

We presentHelix, a blockchain-based consensus protocol forfairordering of transactions among nodes in a distributed network. Helix advances in rounds, in each an elected primary node proposes a potential block (a successive set of transactions). For being included in the blockchain, a block must pass validation by an elected committee of nodes. Nodes have two primary preferences. First, to be elected as committee members. Additionally, because each transaction is associated with one of the network nodes, nodes would like to prioritize their own transactions over those of others. Our definition of fairness incorporates three key elements. First, the process of electing nodes to committees is random and unpredictable. Second, a correlated sampling scheme is used to guarantee random selection and ordering of pending transactions in blocks. Third, transactions are encrypted to hide their associations with nodes and prevent censorship. Through the corresponding threshold decryption process we obtain an unpredictable and non-manipulable randomness beacon, which serves both the election process and the correlated sampling scheme. We define a quantitative measure of fairness in the protocol, prove theoretically that fairness manipulation in Helix is significantly limited, and present experiments evaluating fairness in practice.

While natural language understanding (NLU) is advancing rapidly, today's technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization.This work proposes an approach to representation and learning based on the tenets of embodied cognitive linguistics (ECL).According to ECL, natural language is inherently executable (like programming languages), driven by mental simulation and metaphoric mappings over hierarchical compositions of structures and schemata learned through embodied interaction.This position paper argues that the use of grounding by metaphoric inference and simulation will greatly benefit NLU systems, and proposes a system architecture along with a roadmap towards realizing this vision.

We present Helix, a blockchain-based consensus protocol for fair ordering of transactions among nodes in a distributed network. Helix advances in rounds, where in each round, the primary node (elected among the network nodes) proposes a potential block (a successive set of transactions). In order to be included in the blockchain, a block must pass validation by an elected committee of nodes. Helix nodes are presumed to have two primary preferences. They prefer to be elected as committee members. Additionally, because each transaction is associated with one of the network nodes, they prefer to prioritize their own transactions over those of others. In light of these individual preferences, our definition of fairness incorporates three key elements. First, the process of electing nodes to committees is random and unpredictable. Second, a correlated sampling scheme is used in order to guarantee random selection and ordering of pending transactions in blocks. Third, transactions are encrypted in order to hide their associations with their respective nodes and prevent censorship. Through the corresponding threshold decryption process we obtain an unpredictable and non-manipulable randomness beacon, which serves both the election process and the correlated sampling scheme. We define a quantitative measure of fairness in the protocol, prove theoretically that fairness manipulation in Helix is significantly limited, and present experiments evaluating fairness in practice.