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.

[Q]: 1 Ranks, processes and flat grammar ....................................................................................... 2 2 The Rank-Interpretation Architecture for Multilinear Grammars ......................................... 6 2.1 Outline of the Rank-Interpretation Architecture framework .............................................. 6 2.2 A preliminary note on linearity and hierarchy at the phrasal rank ..................................... 7 2.3 Characterisation of the Rank-Interpretation Architecture .................................................. 9 2.3.1 Background ..................................................................................................................... 9 2.3.2 Formal summary ........................................................................................................... 10 2.3.3 Contrast with traditional views of language architecture .............................................. 11 2.3.4 Search for sui generis properties of ranks ..................................................................... 11 2.4 Procedural perspectives on the rank hierarchy ................................................................. 12 3 The discourse rank .............................................................................................................. 15 3.1 The primacy of discourse patterning ................................................................................ 15 3.2 Intonation of an adjacency pair ........................................................................................ 16 3.3 Chanted ‘call’ intonation .................................................................................................. 18 4 The utterance or text rank ................................................................................................... 20 5 The phrase rank ................................................................................................................... 22 5.1 Characteristics of phrasal structure .................................................................................. 22 5.2 Linear sequences, iteration: regular and subregular grammars ........................................ 23 5.3 A note on long-distance and cross-serial dependencies ................................................... 26 5.4 Prosodic-phonetic interpretation at the phrasal rank ........................................................ 27 6 The word rank ..................................................................................................................... 30 6.1 Flat words ......................................................................................................................... 30 6.2 Flat derivations ................................................................................................................. 30 6.3 Flat compounds ................................................................................................................ 31 6.4 Prosodic-phonetic interpretation at the word rank ........................................................... 31 7 Summary and conclusion .................................................................................................... 33 7.1 From Duality to Multilinear Grammar and Rank Interpretation Architecture ................. 33 7.2 Generalisation to stochastic flat linear models ................................................................. 34 7.3 Future work ...................................................................................................................... 35 8 References ........................................................................................................................... 36
[A]: Multilinear Grammar: Ranks and Interpretations


[Q]: We seek decision rules for prediction-time cost reduction, where complete data is available for training, but during prediction-time, each feature can only be acquired for an additional cost. We propose a novel random forest algorithm to minimize prediction error for a user-specified average feature acquisition budget. While random forests yield strong generalization performance, they do not explicitly account for feature costs and furthermore require low correlation among trees, which amplifies costs. Our random forest grows trees with low acquisition cost and high strength based on greedy minimax cost-weighted-impurity splits. Theoretically, we establish near-optimal acquisition cost guarantees for our algorithm. Empirically, on a number of benchmark datasets we demonstrate superior accuracy-cost curves against state-of-the-art prediction-time algorithms.
[A]: Feature-Budgeted Random Forest


[Q]: We propose a model to learn visually grounded word embeddings (vis-w2v) to capture visual notions of semantic relatedness. While word embeddings trained using text have been extremely successful, they cannot uncover notions of semantic relatedness implicit in our visual world. For instance, visual grounding can help us realize that concepts like eating and staring at are related, since when people are eating something, they also tend to stare at the food. Grounding a rich variety of relations like eating and stare at in vision is a challenging task, despite recent progress in vision. We realize the visual grounding for words depends on the semantics of our visual world, and not the literal pixels. We thus use abstract scenes created from clipart to provide the visual grounding. We find that the embeddings we learn capture fine-grained visually grounded notions of semantic relatedness. We show improvements over text only word embeddings (word2vec) on three tasks: common-sense assertion classification, visual paraphrasing and text-based image retrieval. Our code and datasets will be available online.
[A]:
Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract Scenes