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: How to build a machine learning method that can continuously gain structured visual knowledge by learning structured facts? Our goal in this paper is to address this question by proposing a problem setting, where training data comes as structured facts in images with different types including (1) objects(e.g., <boy>), (2) attributes (e.g., <boy,tall>), (3) actions (e.g., <boy, playing>), (4) interactions (e.g., <boy, riding, a horse >). Each structured fact has a semantic language view (e.g., < boy, playing>) and a visual view (an image with this fact). A human is able to efficiently gain visual knowledge by learning facts in a never ending process, and as we believe in a structured way (e.g., understanding “playing” is the action part of < boy, playing>, and hence can generalize to recognize <girl, playing > if just learn <girl> additionally). Inspired by human visual perception, we propose a model that is (1) able to learn a representation, we name as wild-card, which covers different types of structured facts, (2) could flexibly get fed with structured fact language-visual view pairs in a never ending way to gain more structured knowledge, (3) could generalize to unseen facts, and (4) allows retrieval of both the fact language view given the visual view (i.e., image) and vice versa. We also propose a novel method to generate hundreds of thousands of structured fact pairs from image caption data, which are necessary to train our model and can be useful for other applications.

Answer: SHERLOCK: MODELING STRUCTURED KNOWLEDGE IN IMAGES


Question: Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, but their symbolic nature does not offer graded notions of textual similarity. We propose a method for determining semantic textual similarity by combining shallow features with features extracted from natural deduction proofs of bidirectional entailment relations between sentence pairs. For the natural deduction proofs, we use ccg2lambda, a higherorder automatic inference system, which converts Combinatory Categorial Grammar (CCG) derivation trees into semantic representations and conducts natural deduction proofs. Experiments show that our system was able to outperform other logicbased systems and that features derived from the proofs are effective for learning textual similarity.

Answer: Determining Semantic Textual Similarity using Natural Deduction Proofs


Question: We present a sequential model for temporal relation classification between intrasentence events. The key observation is that the overall syntactic structure and compositional meanings of the multi-word context between events are important for distinguishing among fine-grained temporal relations. Specifically, our approach first extracts a sequence of context words that indicates the temporal relation between two events, which well align with the dependency path between two event mentions. The context word sequence, together with a parts-of-speech tag sequence and a dependency relation sequence that are generated corresponding to the word sequence, are then provided as input to bidirectional recurrent neural network (LSTM) models. The neural nets learn compositional syntactic and semantic representations of contexts surrounding the two events and predict the temporal relation between them. Evaluation of the proposed approach on TimeBank corpus shows that sequential modeling is capable of accurately recognizing temporal relations between events, which outperforms a neural net model using various discrete features as input that imitates previous feature based models.

Answer:
A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events