In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Input: Consider Input: In this paper, we present a conversational model that incorporates both context and participant role for two-party conversations. Different architectures are explored for integrating participant role and context information into a Long Short-term Memory (LSTM) language model. The conversational model can function as a language model or a language generation model. Experiments on the Ubuntu Dialog Corpus show that our model can capture multiple turn interaction between participants. The proposed method outperforms a traditional LSTM model as measured by language model perplexity and response ranking. Generated responses show characteristic differences between the two participant roles.

Output: LSTM based Conversation Models

Input: Consider Input: Korf, Reid, and Edelkamp introduced a formula to predict the number of nodes IDA* will expand on a single iteration for a given consistent heuristic, and experimentally demonstrated that it could make very accurate predictions. In this paper we show that, in addition to requiring the heuristic to be consistent, their formula's predictions are accurate only at levels of the brute-force search tree where the heuristic values obey the unconditional distribution that they defined and then used in their formula. We then propose a new formula that works well without these requirements, i.e., it can make accurate predictions of IDA*'s performance for inconsistent heuristics and if the heuristic values in any level do not obey the unconditional distribution. In order to achieve this we introduce the conditional distribution of heuristic values which is a generalization of their unconditional heuristic distribution. We also provide extensions of our formula that handle individual start states and the augmentation of IDA* with bidirectional pathmax (BPMX), a technique for propagating heuristic values when inconsistent heuristics are used. Experimental results demonstrate the accuracy of our new method and all its variations.

Output: Predicting the Performance of IDA* using Conditional Distributions

Input: Consider Input: How can we automatically discover the most important correspondences between words from two or more languages? How can we do so allowing for correspondences between any subset of languages, without drowning in redundant results, and at the same time maintaining control over the level of detail? These are exactly the questions we answer in this paper. We approach the problem with the Minimum Description Length principle, and give an efficient algorithm for discovering statistically important correspondences. We test the efficacy of our method against a set of Slavic languages. The experiments show our method automatically discovers non-trivial associations, allowing for both quantitative and qualitative analysis of multiple languages.
Output: Discovering Correspondences between Multiple Languages by MDL