The latest work for Question and Answer problems is to use the Stanford Parse Tree. We build on prior develop a new method handle problem with Deep Contextualized Transformer manage some aberrant expressions. also conduct extensive evaluations of SQuAD SwDA dataset show significant improvement over QA classification industry needs. investigate impact different models accuracy efficiency answers...

The rewriting method for text summarization combines the advantage of extractive and abstractive approaches, improving conciseness readability summaries. Exiting systems take sentences as only input rewrite each sentence independently, which may lose critical background knowledge break cross-sentence coherence summary. To this end, we propose contextualized to consume entire document maintain s...

According to the auto-motive model (J. A. Bargh, 1990), intentions and goals are represented mentally and, as representations, should be capable of nonconscious activation by the environmental context (i.e., "priming"). To test this hypothesis, the authors replicated 2 well-known experiments that had demonstrated differential effects of varying the information-processing goal (impression format...

The semantic-tree approach is the most robust method for deciding QBFs. This method is very similar to the DPLL algorithm for evaluating SAT instances. In this paper we show how memorization can be embedded to this method to get better results. In other words we present an algorithm for evaluating QBFs, which is based on an adopted version of semantic-tree method and ZDDs (which are variants of...