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: One of the limitations of semantic parsing approaches to open-domain question answering is the lexicosyntactic gap between natural language questions and knowledge base entries – there are many ways to ask a question, all with the same answer. In this paper we propose to bridge this gap by generating paraphrases of the input question with the goal that at least one of them will be correctly mapped to a knowledge-base query. We introduce a novel grammar model for paraphrase generation that does not require any sentence-aligned paraphrase corpus. Our key idea is to leverage the flexibility and scalability of latent-variable probabilistic context-free grammars to sample paraphrases. We do an extrinsic evaluation of our paraphrases by plugging them into a semantic parser for Freebase. Our evaluation experiments on the WebQuestions benchmark dataset show that the performance of the semantic parser improves over strong baselines.

Output: Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing


Input: Consider Input: Joint matching over a collection of objects aims at aggregating information from a large collection of similar instances (e.g. images, graphs, shapes) to improve maps between pairs of them. Given multiple objects and matches computed between a few object pairs in isolation, the goal is to recover an entire collection of maps that are (1) globally consistent, and (2) close to the provided maps — and under certain conditions provably the ground-truth maps. Despite recent advances on this problem, the best-known recovery guarantees are limited to a small constant barrier — none of the existing methods find theoretical support when more than 50% of input correspondences are corrupted. Moreover, prior approaches focus mostly on fully similar objects, while it is practically more demanding to match instances that are only partially similar to each other (e.g., different views of a single physical object). In this paper, we propose an algorithm to jointly match multiple objects that exhibit only partial similarities, given a few (possibly highly incomplete) pairwise matches that are densely corrupted. By encoding a consistent partial map collection into a 0-1 semidefinite matrix, we propose to recover the ground-truth maps via a parameter-free convex program called MatchLift, following a spectral method that pre-estimates the total number of distinct elements to be matched. Numerically, this program can be efficiently solved via alternating direction methods of multipliers (ADMM) along with a greedy rounding strategy. Theoretically, MatchLift exhibits near-optimal error-correction ability, i.e. in the asymptotic regime it is guaranteed to work even when a dominant fraction 1 − Θ ( log n √ n ) of the input maps behave like random outliers. Furthermore, MatchLift succeeds with minimal input complexity, namely, perfect matching can be achieved as soon as the provided maps form a connected map graph. We evaluate the proposed algorithm on various benchmark data sets including synthetic examples and real-world examples, all of which confirm the practical applicability and usefulness of MatchLift.

Output: Near-Optimal Joint Object Matching via Convex Relaxation


Input: Consider Input: Studies of the overall structure of vocabulary and its dynamics became possible due to creation of diachronic text corpora, especially Google Books Ngram. This article discusses the question of core change rate and the degree to which the core words cover the texts. Different periods of the last three centuries and six main European languages presented in Google Books Ngram are compared. The main result is high stability of core change rate, which is analogous to stability of the Swadesh list.


Output: Dynamics of core of language vocabulary