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: English to Indian language machine translation poses the challenge of structural and morphological divergence. This paper describes English to Indian language statistical machine translation using pre-ordering and suffix separation. The pre-ordering uses rules to transfer the structure of the source sentences prior to training and translation. This syntactic restructuring helps statistical machine translation to tackle the structural divergence and hence better translation quality. The suffix separation is used to tackle the morphological divergence between English and highly agglutinative Indian languages. We demonstrate that the use of pre-ordering and suffix separation helps in improving the quality of English to Indian Language machine translation.

Output: MTIL17: English to Indian Langauge Statistical Machine Translation


Input: Consider Input: There are many complex combinatorial problems which involve searching for an undirected graph satisfying given constraints. Such problems are often highly challenging because of the large number of isomorphic representations of their solutions. This paper introduces effective and compact, complete symmetry breaking constraints for small graph search. Enumerating with these symmetry breaks generates all and only non-isomorphic solutions. For small search problems, with up to 10 vertices, we compute instance independent symmetry breaking constraints. For small search problems with a larger number of vertices we demonstrate the computation of instance dependent constraints which are complete. We illustrate the application of complete symmetry breaking constraints to extend two known sequences from the OEIS related to graph enumeration.

Output: Breaking Symmetries in Graph Search with Canonizing Sets


Input: Consider Input: Extracting time expressions from free text is a fundamental task for many applications. We analyze the time expressions from four datasets and observe that only a small group of words are used to express time information, and the words in time expressions demonstrate similar syntactic behaviour. Based on the observations, we propose a type-based approach, named SynTime, to recognize time expressions. Specifically, we define three main syntactic types, namely time token, modifier, and numeral, to group time-related regular expressions over tokens. On the types we design simple heuristic rules to recognize time expressions. In recognition, SynTime first identifies the time tokens from raw text, then searches their surroundings for modifiers and numerals to form time segments, and finally merges the time segments to time expressions. As a light-weight rule-based tagger, SynTime runs in real time, and can be easily expanded by simply adding keywords for the text of different types and of different domains. Experiment results show that SynTime outperforms state-of-the-art methods on benchmark datasets and tweets data.


Output: Time Expression Analysis and Recognition Using Syntactic Types and Simple Heuristic Rules


