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.
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Question: 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.

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

Question: Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multimodal setup where other relevant modalities accompany language. In this paper, we pose the problem of multimodal sentiment analysis as modeling intra-modality and inter-modality dynamics. We introduce a novel model, termed Tensor Fusion Network, which learns both such dynamics end-to-end. The proposed approach is tailored for the volatile nature of spoken language in online videos as well as accompanying gestures and voice. In the experiments, our model outperforms state-ofthe-art approaches for both multimodal and unimodal sentiment analysis.

Answer: Tensor Fusion Network for Multimodal Sentiment Analysis

Question: The ability to predict which patterns are formed in brain scans when imagining a celery or an airplane, based on how these concepts as words co-occur in texts, suggests that it is possible to model mental representations based on word statistics. Whether counting how frequently nouns and verbs combine in Google search queries, or extracting eigenvectors from matrices made up of Wikipedia lines and Shakespeare plots, these latent semantics approximate the associative links that form concepts. However, cognition is fundamentally intertwined with action; even passively reading verbs has been shown to activate the same motor circuits as when we tap a finger or observe actual movements. If languages evolved by adapting to the brain, sensorimotor constraints linking articulatory gestures with aspects of motion might also be reflected in the statistics of word co-occurrences. To probe this hypothesis 3 20 emotion, face, and hand related verbs known to activate premotor areas in the brain were selected, and latent semantic analysis LSA was applied to create a weighted adjacency matrix. Hierarchically clustering the verbs and modeling their connectivity within a force directed graph, they divide into modules of mouth and hand motion, facial expressions and negative emotions. Transforming the verbs into their constituent phonemes, the corresponding consonant vowel transitions can be represented in an articulatory space defined by tongue height and formant frequencies. Here the vowels appear positioned along a front to back continuum reflecting aspects of size and intensity related to the actions described by the verbs. More forceful verbs combine plosives and sonorants with fricatives characterized by sustained turbulent airflows, while positive and negative emotional expressions tend to incorporate upor downwards shifts in formant frequencies. Suggesting, that articulatory gestures reflect parameters of size and intensity which might be retrieved from the latent semantics of action verbs.

Answer:
When push comes to shove verbs literally shake due to latent semantic parameters of size and intensity